essay on international sign language

Global Coalition for Language Rights

• Sep 21, 2023

Celebrating the International Day of Sign Languages

By Adam Schembri (University of Birmingham)

In 2017, following a proposal from the World Federation of the Deaf (WFD), the United Nations (UN) declared the 23rd of September International Day of Sign Languages . This date was chosen because it is the same date on which the WFD was established in 1951. The WFD is the peak international non-government organisation for national associations of signing deaf people all over the world.

The UN wished to use the day to highlight the objectives set out in article 21 of the UN Convention on the Rights of People with Disabilities (UNCRPD), which states that all signatories should take all appropriate measures to recognise and promote the use of sign languages.

As a hearing linguist who works with deaf individuals and communities to document and describe sign languages, I think it’s particularly important that WFD and UN use the plural ‘sign languages’ in naming the day. The latest edition of the online language catalogue Glottolog lists 219 sign languages used in deaf communities across the globe. This diversity is not as widely appreciated as it should be (i.e., sign language is not universal as sometimes mistakenly believed).

Many hearing people also do not know that sign languages have their own vocabulary and grammar that differ from the surrounding spoken languages. They are distinct languages, not signed forms of spoken languages. Neither of these facts are surprising if one understands that, just like spoken languages, sign languages develop spontaneously wherever deaf people come together to form a community; sign languages are natural languages, not artificial sign systems or conlangs.

This year’s theme for the International Day of Sign Languages is ‘A world where deaf people can sign anywhere’. This reflects the vision of WFD of a more inclusive world in which deaf people and their sign languages are celebrated and used by everyone everywhere. The WFD has called on governments to hold to their commitment to promote sign languages as signatories to the UNCRPD, by ensuring that at least 50% of all children and young people have opportunities to learn their national sign languages.

The WFD has identified that deaf communities in around 60% of the nation states who signed the UNCRPD have not yet achieved any legal recognition of their national sign languages. Perhaps the single most important language right for deaf people is to have access to their community sign language from birth.

Unfortunately, many people are denied access to sign languages because awareness of basic facts about sign languages is relatively low, and due to mistaken beliefs amongst health professionals and educators that using a sign language might hinder spoken language development in deaf children. This too often results in language deprivation, in which children do not gain access to language in the critical early years of life. This leads to lifelong language difficulties and educational disadvantage .

At the 21st General Assembly of the WFD in South Korea this year, deaf representatives from the world’s national associations for deaf people approved the Declaration on the Rights of Deaf Children. This includes 10 points relating to the language rights of deaf children, including Article 7 on the rights of deaf children to be protected from language deprivation. The Global Coalition for Language Rights encourages everyone to read and share this declaration, and show your support for it by adding your signature here: https://wfdeaf.org/rightsdeafchildren/

We have worked with two deaf translators to create a British Sign Language and International Sign version of the GCLR Statement on Understanding and Defending Language Rights . In the future, we hope to expand our work in this area, and collaborate more closely with the WFD and national associations of deaf people to advocate for the language rights of deaf communities everywhere.

Happy International Day of Sign Languages!

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International Sign Language (ISL): Bridging the Silence Across Borders

SignLanguageBlogs

In the vibrant tapestry of sign languages worldwide, there exists a remarkable fusion — a language that transcends borders, unifying deaf individuals from diverse linguistic backgrounds at international events like the Deaf Olympics. This remarkable bridge is International Sign Language (ISL) , an amalgamation of common signs from multiple sign languages, including American Sign Language (ASL) and British Sign Language (BSL), among others, crafting a common method of communication for the global Deaf community.

ISL stands as a testament to the resilience and adaptability of sign languages. It intertwines gestures, signs, and expressions from various sign languages, creating a lingua franca that facilitates communication among individuals from different linguistic backgrounds. At international deaf events, ISL emerges as the shared language, enabling athletes, spectators, officials, and organizers to engage seamlessly, fostering a sense of unity and camaraderie.

One of the most captivating aspects of ISL is its ability to incorporate signs from different sign languages without abandoning their unique characteristics. It doesn’t seek to replace individual sign languages but rather acts as a bridge that allows for communication and understanding despite linguistic diversity. For instance, signs for common concepts like sports, emotions, and greetings are amalgamated, enabling participants to communicate effectively regardless of their native sign language.

During events like the Deaf Olympics, ISL becomes the cornerstone of communication both on and off the field. Athletes, coaches, and supporters utilize ISL to share strategies, celebrate victories, and forge connections. Beyond the competitive arena, workshops, cultural exchanges, and seminars thrive through ISL, facilitating learning and fostering a sense of community among participants from around the world.

The significance of ISL extends beyond communication — it embodies inclusivity and empowerment. By providing a platform for expression and connection, ISL ensures that all participants have equal access and opportunities to engage fully in the event’s proceedings.

However, the journey toward a fully inclusive environment at international Deaf events through ISL is ongoing. Advocacy for the recognition and integration of ISL into these events remains crucial. Efforts to provide adequate interpretation services and ensure accessibility for all individuals involved continue to be focal points for further improvement.

In conclusion, International Sign Language serves as a testament to unity in diversity, embodying the shared experiences and connections among Deaf individuals worldwide at events like the Deaf Olympics and other international Deaf events. Its ability to merge common signs from various sign languages into a coherent method of communication underscores the importance of inclusivity and understanding within the global deaf community. As ISL continues to evolve and find its place on the international stage, its role in fostering unity and empowering individuals stands as a beacon of hope for a more inclusive and diverse world.

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Sign languages are fully-fledged , natural languages with their own dialects – they need protecting

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We most often think of indigenous languages in the context of colonisation – languages used by people who originally inhabited regions that were later colonised. These are the languages that the UN had in mind when it stated a deep concern about the vast number of endangered indigenous languages. And rightly so. More than 2,400 of the about 7,000 languages used around the world today are endangered and most of these are indigenous languages in the above sense.

It’s welcome, then, that 2019 marks the International Year of Indigenous Languages , along with the awareness raising this will bring, as indigenous communities who speak these languages are often marginalised and disadvantaged . But there are other communities who speak indigenous languages that may still not receive much attention: deaf communities around the world who use sign languages.

Linguistic diversity

Sign languages are fully-fledged, complex, natural languages , with their own grammar, vocabulary, and dialects. There are over 140 recorded living sign languages in the world today.

These sign languages have evolved naturally, just like spoken languages. There is no “universal” sign language that is understood by all deaf communities around the world. For example, British Sign Language and American Sign Language are completely unrelated languages; speakers of these two languages cannot understand each other without the help of an interpreter.

Overall, indigenous peoples and their languages drive much of the world’s cultural and linguistic diversity, and sign languages make up only a small portion of this. But the particular diversity that sign languages exhibit contributes tremendously to our understanding of what language is.

Sign languages are acquired and processed in the brain just like spoken languages and fulfil all the same communicative functions. Yet they do so through vastly different means. Sign languages and tactile sign languages have taught us that our capacity for language is independent of any medium.

Any part of our upper body can be involved in language production and can carry grammar, as in American Sign Language, where facial expressions have grammatical functions. We can understand languages not just by hearing, but also through sight and touch. This realisation has contributed greatly to our understanding of the capacity for language in humans.

Read more: What sign language teaches us about the brain

Indigenous sign languages

British Sign Language is one of 11 indigenous languages in the UK. The use of signed communication in the UK can be traced back at least to the 17th century. The parish record of St Martin’s Parish in Leicestershire mentions that in 1575 Thomas Tillsye , who was deaf, used signs “for the expression of his minde instead of words” during his wedding ceremony.

In his account of the great fire of London in 1666, the famed diarist Samuel Pepys mentions one of Sir George Downing’s informants, a deaf boy, who recounted news about the fire using signs: “And he made strange signs of the fire … and many things they understood, but I could not.”

Sign languages evolve naturally when a community has enough deaf members. Sometimes this happens because of a high incidence of deafness in a certain region, as in the case of Martha’s Vineyard Sign Language (now extinct) in the US, Al-Sayyid Bedouin Sign Language in Israel, Ban Khor Sign Language in Thailand, Yucatec Mayan Sign Language in Mexico, and Kata Kolok in Indonesia. These are examples of village sign languages, and they can teach us a lot about inclusion: deaf community members are well integrated into the community because everyone, deaf and hearing, uses the sign language.

Other sign languages have emerged when deaf children get together in educational settings, such as residential schools. For example, when deaf children from all parts of Nicaragua first came together at schools for the deaf in the early 1980s, attempts to teach them Spanish failed. Instead, they created a new sign language, now known as Nicaraguan Sign Language . British Sign Language, too, has historically been learned at residential schools for the deaf, often secretly in dorms because children would be punished if they were caught signing in school.

Read more: Deaf service cuts: a stark reminder of deaf education's troubled history

Depriving deaf children

Deaf communities around the world face many similar challenges to indigenous peoples around the world. Like indigenous peoples , deaf people are often politically and socially isolated , have fewer educational and professional opportunities, and have limited access to information and public services.

While all indigenous languages are indispensable for the communities that use them, this is the case even more so for sign languages. Deaf people cannot hear and so cannot easily access the majority spoken language. This has far reaching implications for language acquisition.

Profoundly deaf children cannot learn spoken languages until they are old enough to be taught reading, writing, lipreading and possibly speaking. In terms of language acquisition, this is much too late – the early weeks, months, and years count – as well as extremely difficult and frustrating for children. As a result, deaf babies, toddlers and young children who are not exposed to a sign language early in life are at risk of being linguistically deprived – they may struggle with language fluency through life and cognitive issues related to language learning.

This is also the case for children with cochlear implants because the success of cochlear implants is highly variable. Early exposure to a sign language not only allows deaf children to grow up bilingual and possibly bicultural, it also provides them with the tools needed to learn the spoken majority language in later childhood. Deaf children with good signing skills typically produce and understand the spoken language around them better than deaf children who do not use a sign language.

So while we celebrate and promote indigenous languages, cultures and peoples in 2019, let’s not forget about signed languages and the unique contributions that they also bring to their users and communities.

• Indigenous languages
• Sign language

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• Corpus ID: 63337383

International Sign: Linguistic, Usage, and Status Issues

• Rachel Rosenstock , Jemina Napier
• Published 2015
• Linguistics

11 Citations

English mouthings in international sign language, more than signs: international sign as distributed practice, task-response times, facilitating and inhibiting factors in cross-signing, the tipping point: on the use of signs from american sign language in international sign, interpreting international sign: mapping the interpreter’s profile, international sign and american sign language as different types of global deaf lingua francas, “do you understand (me)” negotiating mutual understanding by using gaze and environmentally coupled gestures between two deaf signing participants, family language policy on holiday: four multilingual signing and speaking families travelling together, emergence and evolutions: introducing sign language sociolinguistics, innovations in deaf studies: critically mapping the field, 23 references, interpreting into international sign pidgin: an analysis, teaching and learning signed languages : international perspectives and practices, comprehension of sign language interpreting: deciphering a complex task situation, cooperation in interpreter-mediated monologic talk, sign language comprehension: the case of spanish sign language., “making meaning”: communication between sign language users without a shared language, signed languages and globalization, the basic variety (or: couldn't natural languages be much simpler), the role of iconicity in international sign, determining aspects of text difficulty for the sign language of the netherlands (ngt) functional assessment instrument, related papers.

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International Sign: a practicioner's perspective

A history and practical guide to IS published in the Registry of Interpreters for the Deaf Journal of Interpretation, 2002

Related Papers

Eliete F. Miranda

Understanding International Sign: A Sociolinguistic Study

Lori Whynot

In Understanding International Sign, the author examines International Sign (IS) to determine the extent to which signers from different countries comprehend it. She focuses exclusively on expository lecture IS used in conference settings and presents the first empirical research on its effectiveness for communicating rich information to diverse audience members. International Sign is regarded as a lingua franca that is employed by deaf people to communicate with other deaf people who do not share the same conventionalized local sign language. Contrary to widely-held belief, sign languages are not composed of a unified system of universal gestures—rather, they are distinctly different, and most are mutually unintelligible from one another. The phenomenon of IS has emerged through increased global interaction during recent decades, driven by a rise in the number of international conferences and events and by new technologies that allow for enhanced global communication. IS is gaining acceptance as communicative access to conference audience members who do not have knowledge of the designated conference languages, and it is being recruited for use due to the prohibitive expense of providing interpreting services in numerous different sign languages. However, it is not known how well audience members understand IS, and it may actually limit equal access to the interpreted information for some monolingual audience members. The study compares IS to native sign languages and analyzes the distribution of linguistic elements in the IS lexicon and their combined effect on comprehension. Her findings indicate that varied monolingual sign language participants understand much less of IS presentations than has been previously assumed- particularly detailed information; Also, multilingual audiences who know BSL, ASL (and their related languages), and English are more successful at understanding information via IS. Whynot’s research has crucial implications for expository IS usage, training, and interpreting, and it sheds light on the strengths and weaknesses inherent in cross-linguistic, signed contact settings.

Martje Hansen

This article discusses if International Sign should be considered a language system like ASL or French or if it is something else, e.g. a pdigin, a 'lingua franca', or a communication tool ecc.

Brain and Language

Emmanuel Mellet

Telling, Showing, Representing: Conventions of Conventions of lexicon, depiction and metaphor in International Sign expository text.

Lexical frequency study of International Sign lectures (expository texts)

Debra Russell

Across the globe, there are increased opportunities for Deaf interpreters to provide interpreting services in a range of settings, from one-to-one interactions between Deaf and nondeaf participants in medical, legal, and/ or employment settings, to working with larger audiences in educational and conference settings. While frequently the work of Deaf interpreters and their co-interpreters occurs in the national signed language of the country (for example, into British Sign Language [BSL] or Langue des Signes Québécoise), there are other occasions when Deaf interpreters provide interpreting services into what is known as International Sign (IS). Recently, research attention has turned to the work of interpreters providing IS, with the goal of increasing our understanding of this unique language contact between users of different signed languages. In this study, we examined the work of IS teams providing interpretation at an international conference. Previous research emphasized clear, communicative IS as being visually motivated, without further descriptions of the embodied nature of experience and its role in motivating IS production (Allsop, Woll, & Brauti, 1995; Suppalla & Webb, 1995; Rosenstock, 2008). The term visual could mean iconic language use, although iconicity is complex and may be further categorized (Taub, 2001). Depiction is one type of iconicity where, in addition to their usual function , verbs also depict the event they encode (Dudis, 2007) and usefully describes experientially motivated decisions in IS interpreting. Using a We thank Keith Gamache, the research assistant who worked on this project at Gallaudet University. We also thank the ESRC and SSRC for funding through the Collaborative Visiting Fellow program, which supported the collection of these data.

Mike Gulliver

In this paper we draw strong parallels between Sign Language Peoples (SLPs) and First Nation peoples. We argue that SLPs (communities defining themselves by shared membership in physical and metaphysical aspects of language, culture, epistemology, and ontology) can be considered indigenous groups in need of legal protection in respect of educational, linguistic, and cultural rights accorded to other First Nation indigenous communities. We challenge the assumption that SLPs should be primarily categorised within concepts of disability. The disability label denies the unique spatial culturolinguistic phenomenon of SLP collectivist identity by replicating traditional colonialist perspectives, and actively contributing to their ongoing oppression. Rather, SLPs are defined spatially as a locus for performing, building, and reproducing a collective topography expressed through a common language and a shared culture and history.

Trevor A Johnston

Trevor A Johnston , Adam Schembri

This is first comprehensive introduction to the linguistics of Auslan, the sign language of Australia. Assuming no prior background in language study, it explores each key aspect of the structure of Auslan, providing an accessible overview of its grammar (how sentences are structured), phonology (the building blocks of signs), morphology (the structure of signs), lexicon (vocabulary), semantics (how meaning is created), and discourse (how Auslan is used in context). The authors also discuss a range of myths and misunderstandings about sign languages, provide an insight into the history and development of Auslan, and show how Auslan is related to other sign languages, such as those used in Britain, the USA and New Zealand. Complete with clear illustrations of the signs in use and useful further reading lists, this is an ideal resource for anyone interested in Auslan, as well as those seeking a clear, general introduction to sign language linguistics.

Anna Puupponen

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International Day of Sign Languages – 23 September

23 september 2024 hora: 8:00 am - 5:00 pm.

Sign languages unite us!

The International Day of Sign Languages is a unique opportunity to support and protect the linguistic identity and cultural diversity of all deaf people and other sign language users. During the   2023 celebration  of the International Day of Sign Languages, the world will once again highlight the unity generated by our sign languages. Deaf communities, governments and civil society organizations maintain their collective efforts – hand in hand – in fostering, promoting and recognizing national sign languages as part of their countries’ vibrant and diverse linguistic landscapes.

According to the World Federation of the Deaf, there are more than 70 million deaf people worldwide. More than 80% of them live in developing countries. Collectively, they use more than 300 different sign languages.

Sign languages are fully fledged natural languages, structurally distinct from the spoken languages. There is also an international sign language, which is used by deaf people in international meetings and informally when travelling and socializing. It is considered a pidgin form of sign language that is not as complex as natural sign languages and has a limited lexicon.

The  Convention on the Rights of Persons with Disabilities   recognizes and promotes the use of sign languages. It makes clear that sign languages are equal in status to spoken languages and obligates states parties to facilitate the learning of sign language and promote the linguistic identity of the Deaf community.

The UN General Assembly  has proclaimed 23 September as the International Day of Sign Languages   in order to raise awareness of the importance of sign language in the full realization of the human rights of people who are deaf.

The  resolution   establishing the day acknowledges that early access to sign language and services in sign language, including quality education available in sign language, is vital to the growth and development of the deaf individual and critical to the achievement of the internationally agreed development goals. It recognizes the importance of preserving sign languages as part of linguistic and cultural diversity. It also emphasizes the principle of “nothing about us without us” in terms of working with Deaf communities.

The proposal for the Day came from the  World Federation of the Deaf  (WFD), a federation of 135 national associations of deaf people, representing approximately 70 million deaf people’s human rights worldwide. The resolution  A/RES/72/161   was sponsored by the Permanent Mission of Antigua and Barbuda to the United Nations, co-sponsored by 97 United Nations Member States and adopted by consensus on 19 December 2017.

The choice of 23 September commemorates the date that the WFD was established in 1951. This day marks the birth of an advocacy organization, which has as one of its main goals, the preservation of sign languages and Deaf culture as pre-requisites to the realization of the human rights of deaf people.

The International Day of Sign Languages was first celebrated in 2018 as part of the   International Week of the Deaf .

The International Week of the Deaf was first celebrated in September 1958 and has since evolved into a global movement of Deaf unity and concerted advocacy to raise awareness of the issues deaf people face in their everyday lives.

More information here.

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Home > Society for American Sign Language Journal > Vol. 5 > No. 2 (2021)

Society for American Sign Language Journal

The success in creating an international perspective on sign language policy.

Beverly Buchanan , Lamar University

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Buchanan, Beverly (2021) "The Success in Creating an International Perspective on Sign Language Policy," Society for American Sign Language Journal : Vol. 5: No. 2, Article 5. Available at: https://open.clemson.edu/saslj/vol5/iss2/5

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SECRETARY-GENERAL’S MESSAGE ON THE INTERNATIONAL DAY OF SIGN LANGUAGES

New York, 23 September 2020

On the International Day of Sign Languages this year, we find ourselves in the midst of a pandemic that has disrupted and upended lives everywhere, including the lives of the deaf community.

It has been encouraging to see some countries providing public health announcements and information on COVID-19 with national sign language...

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The History and Evolution of American Sign Language: A Journey of Recognition and Empowerment

• April 7, 2023

Introduction

American Sign Language (ASL) is a complex, fully developed visual-spatial language utilized by the Deaf and hard-of-hearing communities in the United States and parts of Canada. ASL has a rich history that reflects the resilience and determination of the Deaf community in seeking recognition and social equality. This essay will provide an overview of the origins of ASL, its development over time, the influences of other sign languages and linguistic communities on its evolution, and the key historical figures, educational institutions, and milestones that have contributed to its growth and recognition as a distinct and valuable language.

Origins of ASL

The roots of ASL can be traced back to the early 19th century, with the merging of local sign languages and the Old French Sign Language (OFSL) brought to the United States by Laurent Clerc, a Deaf educator from France (Lane, Hoffmeister, & Bahan, 1996). In 1817, Clerc and Thomas Hopkins Gallaudet, an American educator, established the American School for the Deaf (ASD) in Hartford, Connecticut, which became the cradle of ASL (Lane et al., 1996). The ASD provided an environment where the combination of OFSL and the regional sign languages of Martha’s Vineyard, Henniker, and Sandy River Valley, among others, gave birth to what we now know as ASL (Groce, 1985).

Development of ASL

ASL continued to develop and evolve throughout the 19th and 20th centuries. Its expansion was propelled by the establishment of more schools for the Deaf, such as the New York Institution for the Deaf in 1818 and the Pennsylvania School for the Deaf in 1820 (Gannon, 1981). As the Deaf community grew, so did the linguistic diversity of ASL, as regional variations and dialects emerged (Lucas, Bayley, & Valli, 2001).

One of the most significant milestones in the development of ASL was the publication of the first ASL dictionary by William Stokoe in 1960 (Stokoe, 1960). Stokoe’s work demonstrated that ASL possessed a linguistic structure and grammar distinct from English, contributing to its recognition as a bona fide language (Stokoe, 1960). Subsequent research further established ASL as a complex and robust language with its own syntax, morphology, phonology, and semantics (Klima & Bellugi, 1979).

Influences of Other Sign Languages and Linguistic Communities

Various sign languages and linguistic communities have influenced ASL throughout its history. As previously mentioned, OFSL played a pivotal role in the formation of ASL, providing a foundation upon which regional sign languages could merge (Lane et al., 1996). Additionally, ASL has been influenced by Black American Sign Language (BASL), which developed among African American Deaf communities during segregation (Lucas, Bayley, & Valli, 2001). The two languages share many similarities, but BASL exhibits unique phonological, lexical, and syntactic features that reflect its users’ distinct experiences and cultural identities (Lucas et al., 2001).

Key Historical Figures, Educational Institutions, and Milestones

The history of ASL is marked by the contributions of key figures, educational institutions, and milestones that have shaped its growth and recognition. Thomas Gallaudet and Laurent Clerc were instrumental in founding ASD, which served as the birthplace of ASL and a model for other Deaf schools across the country (Lane et al., 1996). Another notable figure is Edward Miner Gallaudet, Thomas Gallaudet’s son, who founded Gallaudet University in 1864, the world’s first and only liberal arts university for the Deaf (Gannon, 1981). Gallaudet University has since become a hub for research and innovation in ASL and Deaf culture.

Throughout the 20th century, several milestones contributed to the recognition and standardization of ASL. The National Association of the Deaf (NAD), founded in 1880, advocated for the rights of the Deaf community and promoted ASL as a legitimate language (Gannon, 1981). The work of William Stokoe and the publication of the first ASL dictionary in 1960 served as a turning point in the linguistic recognition of ASL (Stokoe, 1960). Later, the establishment of the Linguistics Research Laboratory at Gallaudet University in 1975 further advanced the study and documentation of ASL (Klima & Bellugi, 1979).

In 1990, the Americans with Disabilities Act (ADA) was passed, which granted Deaf individuals legal rights and protections, including the right to access ASL interpreters (ADA, 1990). This legislation marked an essential milestone in the recognition of ASL and the empowerment of the Deaf community.

The history of American Sign Language is a testament to the resilience, adaptability, and determination of the Deaf community in the United States. From its origins as a fusion of OFSL and local sign languages to its development and evolution over time, ASL has grown into a robust and distinct language that serves as a cornerstone of Deaf culture. The contributions of key historical figures, educational institutions, and milestones have been integral to the recognition of ASL as a valuable and legitimate language. As research and advocacy continue, ASL will continue to evolve and empower its users for future generations.

ADA. (1990). Americans with Disabilities Act of 1990. Pub. L. No. 101-336, 104 Stat. 327.

Gannon, J. R. (1981). Deaf Heritage: A Narrative History of Deaf America. National Association of the Deaf.

Groce, N. E. (1985). Everyone Here Spoke Sign Language: Hereditary Deafness on Martha’s Vineyard. Harvard University Press.

Klima, E. S., & Bellugi, U. (1979). The Signs of Language. Harvard University Press.

Lane, H., Hoffmeister, R., & Bahan, B. (1996). A Journey into the DEAF-WORLD. DawnSignPress.

Lucas, C., Bayley, R., & Valli, C. (2001). Sociolinguistic Variation in American Sign Language. Gallaudet University Press.

Stokoe, W. C. (1960). Sign Language Structure: An Outline of the Visual Communication Systems of the American Deaf. Studies in Linguistics: Occasional Papers (No. 8). University of Buffalo.

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Importance Of Sign Language

Content Sign Language • Sign language (SL) is a visual gesture language which includes face, hands and arms to convey thoughts and meanings. • It is developed in deaf communities which include deaf people, their friends and families. They use their hands, face, arm and body for communication. • Sign languages are complete natural languages having their own syntax and grammar. • Many hearing people have the false impression that Sign Language is a worldwide universal language , but Sign languages are not universal, each one will have differences in syntax, lexicon, etc. These languages changes from one region to another. Every country is having their own sign language which varies from another country in syntax and grammar. • There are about more than 200 languages available all around the world. Sign language which is used in America is known as American Sign Language (ASL) whereas sign language which is used in England is known as British Sign Language (BSL) and in India Indian Sign Language (ISL) is used. There are other sign languages available such as chinese, German, French, Italian, and Turkish Sign Language. • • Sign languages have not been studied as extensively as spoken languages, and the field of research is still fairly young. A good understanding of how sign languages are used is necessary for creating a good translation system. Sign languages are vastly different from spoken languages; some of the methods used in spoken language translation/recognition

George Veditz's Contributions To The Deaf

Veditz is very clear about wanting to preserve the beauty of sign language without asking them to do anything. Veditz says in his speech, “We need these films to preserve and pass on our beautiful signs. As long as there are deaf people on earth there will be signing. And as long as we have our films, we can preserve our beautiful signs in their old purity.”

Gallaudent: The American School For The Deaf

In 1830, Gallaudent retired from ASD and in 1850 Clerc out from the school and end his taught at the ASD. In 1863, The American School for the Deaf had been established increase Twenty- two branches in the United States. Before 1880 Gallaudent’s son name Edward, he was a person who establish Gallaudet College and he also can use ASL same as use English Language. When 1880 come a new teaching method call oralism that focus on teaching how use speak and lip read with no sign languages. In 1960, ASL was became an official Language and still grow.

The Case Who Was Laurent Clerc And What Did He Do To Deaf Education

-In the 19th century the international congress on education of the deaf in Milan, Italy came up with the decision on banning the use of sign language

Through Deaf Eyes Documentary Summary

Sign language was their true language. It was their natural way of communicating . I believe that it should be the deaf person's choice about learning to speak. Forcing it on someone and prohibiting from using their own language was not the right way to go about things. I'm glad that the time where the oral method was forced on the deaf is over and that they are now free to use sign language.

How Did Tressa Bower's Life Change

The summer Alandra turned two years old, Tressa and Alandra, accompanied by Linda and Joy, attended a two-week long seminar for parents of deaf children at the Illinois School for the Deaf in Jacksonville. The psychologist there highly recommended American Sign Language as the main form of communication, but Tressa disregarded his advice, wanting to stick to the oral method, which they had been working so hard on,

Through Deaf Eyes Documentary Analysis

Someone as Alexander Graham Bell, who is naturally considered one of the greatest inventors in the hearing world, believed that the language used by the deaf community was not a language. The hearing world is the most dominant one, there is no doubt. However, there has to be an understanding that not everyone who is different from the “typical” is “atypical”. A language is nothing but patterns of signs, symbols, and/or sounds that are used to convey meaning. In what manner does sign language not fit the category of a language?

Sign Language In Lynn's Deaf Like Me

The story of Lynn Spradley’s journey is for every parent who believes that their child isn’t normal. I learned a great deal about what it truly means to be deaf from this book. Reading this story brought out much emotion as the story progressed. Lynn’s parents Tom and Louise reaction of every parent’s worst thought when having a child. Everyone believes that there child is going to be healthy and fully functioning ready to be a part of the world.

Legacy Behind Asl Essay

Legacy Behind ASL Imagine how communication is done between those people who do not have the ability to hear or speak. Of course, there must be some ways of communication that are convenient for the deaf people to communicate. The founder of the American Sign Language , Thomas Hopkins Gallaudet, discovered the new way of communicating between the deaf people. Thomas Hopkins Gallaudet was inspired by a young deaf girl named Alice Cogswell, which was his next door neighbor.

Speaking In The Presence Of A Deaf Person Essay

It is difficult to stop because English was my first language and I am so used to using it in every other class. Breaking the habit of talking is possible, but it is a challenge because I am not as used to signing as I am speaking. By choosing to sign more instead of talking you show improvement in ASL because you can naturally and instinctively respond to others in ASL

Being Deaf African American Research Paper

There is no such thing as Black Sign Language, but there is a Black way of signing used by Black Deaf people in their own culture, among families, friends, at gatherings, at the Deaf Clubs, and at the residential school for the Deaf. “1996 a new controversy arose when the Oakland, California school district became the first school system in the United States to recognize Black English, or Ebonics, as a language” (Jankowski, 1997, p.

The Pros And Cons Of American Sign Language

American sign language or ASL is a complete language that uses signs made by hand gestures, facial expressions and your body posture. It is the primary communication of those who are deaf or hard of hearing. Sign language is universal. Where did this beautiful language come from?

Deaf Personal Statement Essay

With this experience, it had allowed me the space to utilize the skills that I have to acquire through my educational program while attending QCC. I have found this experience to be rewarding which gave me great insight into my future of being a Sign Language Interpreter. By me working side by side with members of the Deaf community, it has shown me …. And with this I have a better understanding what it means to be a sign language interpreter. Therefore, my hope is that by me being accepted to your program I can further learn more about the community and the different ways in which I can be helpful towards members within the Deaf community.

How Does Language Affect Communication

Language is a system of communication consisting of sounds, words and grammar, or the system of communication used by the people of a particular country or profession. Even animals communicate. Birds use sound and movement to transfer information. Likewise human beings use sound and movement like speech and gesture to communicate. Language is the fundamental factor leading and affecting communication.

Interactionist Theory Of Language Development

Language development is a critical part of a child’s overall development. Language encourages and supports a child’s ability to communicate. Through language, a child is able to understand and define his or her’s feelings and emotions. It also introduces the steps to thinking critically as well as problem-solving, building and maintaining relationships. Learning a language from a social perspective is important because it gives the child the opportunity to interact with others and the environment.

Reflection About Language

Language is an abstract concept which needed by people to communicate. Language has an intrinsic meaning which represents an image and it is also symbolic however not only symbolic. Language is also a complex system and it is creative and productive meaning that you can product many words. Language does not only include objects but also includes all the images and concepts of the world. There is an abstraction of a real world.

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• Hearing impairment
• Deaf culture
• Sign language
• Communication

American Sign Language and Its Importance Essay

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Deaf people cannot speak, communicate, and perceive the world fully. They are limited in their perception: vision and sensation are the only primary channels for them to obtain information about the world. However, one of the main problems is the absence of language, which is crucial for developing higher cognitive skills. However, sign language helps maintain and develop the cognitive abilities of deaf people; in that way, they must be taught sign language.

Language deprivation is a significant danger for deaf people: it results from the absence of language learning during childhood. Imagine that the child is growing up in wild conditions, where they cannot hear any speech, any word of mouth. This is what happens to those deaf children who have not learned sign language when they are primarily open to this (N. K. Caselli et al., 2020). All children have increased neurological sensitivity to language learning, which is why they learn languages easier than older people. If they do not learn a language in this period, their cognitive abilities become impaired, not only memory but also the ability to form conscious thoughts. They become more like animals, not humans; this may sound dreadful, but this is the reality of kids who cannot think consciously and memorize what they see. If deaf child is not taught sign language, they must rely only on their vision and fundamental patterns, such as objects’ forms, colors, and quantities. They are similar, in that way, to primitive people who were not able to speak.

Deaf people have weaker memory due to their inability to communicate using ordinary language. Teaching sign language from childhood may help prevent these problems and restore normal memory development. However, their parents, especially those who are hearing, usually have low proficiency in sign language (Bansal et al., 2021). Sometimes they are not bothered to teach their kid sign language, but the consequences of such inactivity are awful. Their working memory becomes weaker, and they cannot operate by word constructions that are accessible to their hearing mates. Compare, thus, the opportunities for such deaf people when they become teens and adults: they will be much worse than those for hearing people. Deaf person is limited not only in their perception but in their language and cognitive abilities; however, this issue may be solved by teaching American Sign Language from childhood.

Consider that American Sign Language is essential for deaf people: probably, even the most necessary skill at all. It allows them to close the gap of language ignorance and learn how to speak even without the ability to hear and produce conscious voices. This is why systems that help deaf children to learn American Sign language are in demand: an example is CopyCat, a sign language recognition system that is easily managed via its visual interface (N. K. Caselli et al., 2020). When deaf children start to learn sign language in early childhood, they have a vocabulary comparable to hearing children (N. Caselli et al., 2021). Thus, it solves all cognitive problems which threaten deaf children, enabling them to grow up as fully conscious human beings with opportunities equal to those of hearing ones.

To conclude, I would emphasize the necessity of American Sign Language development and distribution: without that, our deaf children are fated on ignorance and even semi-wildness. Language is a crucial element of humanity; it is necessary for brain development, as neither memory nor higher brain functions can work without it. Sign language closes the gap of language ignorance, enabling deaf people to learn how to speak and form conscious thoughts. American Sign Language is crucial for the United States deaf community, as it helps them avoid language deprivation and master conscious thinking.

Bansal, D., Ravi, P., So, M., Agrawal, P., Chadha, I., Murugappan, G., & Duke, C. (2021). CopyCat: Using sign language recognition to help deaf children acquire language skills. Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems .

Caselli, N. K., Hall, W. C., & Henner, J. (2020). American sign language interpreters in public schools: An illusion of inclusion that perpetuates language deprivation . Maternal and Child Health Journal , 24 (11), 1323–1329.

Caselli, N., Pyers, J., & Lieberman, A. M. (2021). Deaf children of hearing parents have age-level vocabulary growth when exposed to American Sign Language by 6 months of age . The Journal of Pediatrics , 232 , 229–236.

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Exploring Chinese Social Media in International Language Management for Museum Industry Training

25 Pages Posted: 6 Sep 2024

Dhurakij Pundit University

Yi Ying Tsai

Tamkang University

Ling-Ge Chen

Tai-liang wu.

Tainan University of Technology

The surge in documenting and sharing personal museum visits on social media platforms has been notable in recent times. However, a significant gap persists in research regarding the influence of social media on the perceptions and decisions of potential visitors in the context of museum tour guide staff training, particularly in international language management and business training. This study aims to explore the efficacy of Xiaohongshu as a video-assisted tool in enhancing the creativity and oral proficiency of museum tour guide learners. The research cohort comprises 120 museum tour guide learners, evenly distributed into control and experimental groups, with 60 participants in each. The experimental group utilizes Xiaohongshu for instructional purposes, while the control group adheres to conventional teaching methodologies. Both groups undergo pre-tests and post-tests. Survey results reveal that museum tour guide learners engaged in the Xiaohongshu experiment demonstrated significant improvements in both creativity and oral proficiency compared to their counterparts subjected solely to traditional teaching methods. This study advocates for the integration of Xiaohongshu as a video-assisted tool within museum tour guiding training programs to enhance the creativity and oral proficiency of museum tour guide learners, with implications for international language management and business training.

Keywords: Xiaohongshu, Museum Business, Creativity Communication Skills, International language management

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Instant Sign Language Recognition by WAR Strategy Algorithm Based Tuned Machine Learning

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• Shahad Thamear Abd Al-Latief   ORCID: orcid.org/0009-0003-9141-7951 1 ,
• Salman Yussof   ORCID: orcid.org/0000-0002-2040-4454 2 ,
• Azhana Ahmad   ORCID: orcid.org/0000-0003-1149-4053 3 ,
• Saif Mohanad Khadim   ORCID: orcid.org/0009-0009-5090-8942 1 &
• Raed Abdulkareem Abdulhasan   ORCID: orcid.org/0000-0001-6478-8990 4  

Sign language serves as the primary means of communication utilized by individuals with hearing and speech disabilities. However, the comprehension of sign language by those without disabilities poses a significant challenge, resulting in a notable disparity in communication across society. Despite the utilization of numerous effective Machine learning techniques, there remains a minor compromise between accuracy rate and computing time when it comes to sign language recognition. A novel sign language recognition system is presented in this paper with an exceptionally accurate and expeditious, which is developed upon the recently devised metaheuristic WAR Strategy optimization algorithm. Following the preprocessing, both of spatial and temporal features has been extracted using the Linear Discriminant Analysis (LDA) and Gray-level cooccurrence matrix (GLCM) methods. Afterward, the WAR Strategy optimization algorithm has been adopted in two procedures, first in optimizing the extracted set of features, and second to fine-tune the hyperparameters of six standard machine learning models in order to achieve precise and efficient sign language recognition. The proposed system was assessed on sign language datasets of different languages (American, Arabic, and Malaysian) containing numerous variations. The proposed system attained a recognition accuracy ranging from 93.11% to 100% by employing multiple optimized machine learning classifiers and training time of 0.038–10.48 s. As demonstrated by the experimental outcomes, the proposed system is exceptionally efficient regarding time, complexity, generalization, and accuracy.

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1 Introduction

Communication has a pivotal role in establishing and maintaining interpersonal relationships, that exerts a substantial influence on individuals’ lives through its facilitation of knowledge acquisition and sharing, promotion of social interaction, cultivation of relationship growth, and provision of a means for individuals to express their feelings and needs [ 1 ]. In contrast to most individuals who use verbal communication, individuals with hearing and speaking limitations employ a distinct form of nonverbal communication referred to as Sign Language (SL). This form of language plays a crucial role in facilitating communication for people who encounter challenges in verbal or auditory expression. In contrast to spoken language, the comprehension of sign language does not depend on sound perception or vocalization. In contrast, those who are deaf or mute employ a coordinated array of hands shapes, orientations, and movements that rely on many body parts such as fingers, hands, arms, head, torso, and facial expressions. This multifaceted approach enables them to effectively communicate messages, thereby establishing sign language as a visual form of communication [ 2 ]. The linguistic studies of sign language originated in the 1970s [ 3 ], which has demonstrated that sign language shares similarities with spoken languages but is distinct from them in terms of its vocabulary and grammar. Signs are created using a finite collection of gestural features, similar to how a small number of sounds can produce millions of words in spoken languages. Sign language undergoes natural evolution and growth over time and across different geographical locations. Numerous countries possess their own distinct national sign language, exhibiting regional and domestic variations [ 4 ]. Whereas, depending on the report of the World Federation of the Deaf there are more than 70 million individuals in the world own a speaking and hearing disability which uses over than 300 type of sign language [ 5 ]. Nevertheless, this mode of communication is not widely embraced by those who do not have hearing or speaking difficulties, and a minority of them has the ability to comprehend and acquire proficiency in it. This observation highlights an authentic disparity in communication with the individuals who experience hearing and speaking impairments and the broader society [ 6 ]. Hence, there is a crucial need for an automated system capable of precisely identifying and translating sign language in order to overcome these obstacles and create a smooth platform for communication between individuals who are deaf/mute and those who are able to hear and speak. Furthermore, in order to guarantee equitable access to information for those who are deaf or mute, akin to their counterparts [ 7 ]. Sign language recognition encompasses several crucial sub-fields, including detection, tracking, pose estimation, gesture recognition, and pose recovery. Many Machine learning models have been widely employed in human–computer interaction applications, where these sub-fields are substantially applied. However, the task of recognizing sign language using machine learning is complicated and presents various problems that might have a substantial impact on the outcomes. Whereas the signs in this language lack a direct correspondence to a particular word. Hence, the identification of sign language is a multifaceted procedure that goes beyond the mere replacement of individual signs with their respective spoken language equivalents. This phenomenon can be linked to the presence of unique vocabulary and grammatical structures in sign languages, which are not limited to any specific spoken language [ 8 ]. Moreover, other challenges encompass several aspects, namely the scarcity and imbalance of datasets, the unpredictability in sign language due to environmental conditions, the presence of dynamic indicators that might lead to occlusion, the extraction of features, the time necessary, the complexity involved, and the processing cost [ 9 ]. The feature extraction is considered to be as an initial stage prior to employing machine learning techniques for classification purposes in most image recognition systems. In contrast, deep learning, a distinct type of machine learning, involves the direct extraction of features and representations from data [ 10 ]. This is achieved by encoding the entirety of images as points inside a high-dimensional space and utilizing them as a cohesive unit for the purpose of training [ 11 ]. Nevertheless, the successful training of these models frequently necessitates a substantial amount of annotated training data and significant computational resources [ 12 ]. An extra procedure so called feature optimization or feature selection has been adopted in several recognition systems, which is used to optimize or select the most effective collection of extracted features in order to reduces the dimensions so minimize the time [ 13 ]. Therefore, the primary aim of this framework is to augment the caliber of the features employed in machine learning models, thereby enhancing the accuracy, generalization, and overall performance of the models [ 14 ]. Moreover, another performance enhancement of the recognition systems has employed the concept of hyperparameter optimization of machine learning models. Its main intent is to identify the most favorable parameter values that exhibit high level of success for the model. Metaheuristic optimization algorithms are the most widely recognized and commonly employed for the purposes of feature optimization and machine learning hyperparameter tuning [ 15 ]. This is due to its ability to find the best set of solutions in order to provide a solution for complicated unsolved problems by traditional methods in many domains. Moreover, many adversarial attacks can face any classification system using machine learning such as inference attacks [ 16 ], Image Perturbation Attacks, Pose Manipulation, Temporal attacks such as motion blur, Data Poisoning, Backdoor Attacks that lead to misclassification, and Privacy and Inference Attacks [ 17 , 18 , 19 , 20 , 21 , 22 ]. One of the most protection against these attacks is the efficient extraction of features that represent the data in an effective manner.

This work introduces a novel technique for recognizing static signs in sign language, namely those representing alphabetics and numerals. The extracted features have been optimized using the recently created metaheuristic WAR Strategy optimization algorithm. Furthermore, the WAR Strategy algorithm has been utilized to optimize the hyperparameters of six machine learning algorithms. The system provided successfully addresses the challenges of generalization, computing cost, and accurately recognizes sign language in different poses, orientations, and illuminations with minimal time requirements.

The subsequent sections of this research have been organized as follows: In Sect.  3 , the metaheuristic WAR Strategy optimization algorithm is described. Section  4 describes the proposed methodology for the proposed static sign language recognition system. The outcomes obtained by implementing the introduced system on three distinct sign language datasets are detailed in Sect.  5 . Future research and a succinct summary of the conclusions are included in Sect.  6 .

2 Literature Review

Sign language recognition is an extremely competitive subfield of gesture recognition within the academic community. The detection of the gesture may involve a variety of techniques, like the application of sensory hardware which incurs substantial costs and presents fewer practical considerations in real-life circumstances. As a result, scholars have implemented computer vision algorithms to attain the highest level of accuracy in sign language recognition. As a result, machine learning techniques have gained significant attention, leading to the development of numerous machine learning models, particularly deep ones, for the purpose of recognizing sign language in its various forms (static and dynamic), across different languages and countries. However, developing a high-performing deep learning model for sign language recognition remains difficult, owing to the sign language’s complexity, its reliance on spatial and temporal features, and the computational time and cost associated with deep learning models. Due to these constraints, the researchers were compelled to utilize metaheuristic algorithms in conjunction with machine learning models in order to accurately identify the sign language.

In [ 23 ], presented a gesture recognition system that employs the Cuckoo Search algorithm along with to minimize the complex trajectory and the SURF transform in the Hidden Markov model (HMM). The main goal was to decrease the features dimensions in different, illumination, orientations, and shapes of gestures. An 80.16% recognition accuracy achieved from this system, that cannot be accepted in real time application, and it has not been evaluated in recognizing the sign language.

In [ 24 ], the static Indian sign language recognition system has been introduced depending on the use of an Improved Genetic Algorithm to perform features selection after extraction depending on the contour model and canny detector. A feed-forward neural network has been adopted for classification and came up with a 74% recognition accuracy.

In [ 25 ] the metaheuristic Crow Search Algorithm (CSA) has been adopted to identify and select the best parameters for the convolution neural networks for gesture recognition in Human Computer Interaction (HCI). The system has shown a high recognition accuracy equal to 100% on the public from Kaggle dataset. Nevertheless, the system suffers from hardware and computational cost since it has been executed in the Google Collab’s GPU-based cloud framework. Additionally, the system has not undergone evaluation on several sign languages of different sorts, resulting in limited generality.

In [ 26 ], the gesture has been recognized using a proposed Lightboost based Gradient boosting machine (LightGBM) with a parameter enhancement method depending on a developed Memetic Firefly algorithm. Their main aim in this work was to reduce the computational cost while providing a high performance of recognition, in which it shows a 99.36%, 99%, 99%, and 99% in term of Accuracy, Precision, Recall, and F1-measure sequentially. The introduced machine model in this work owns a high count of parameters that may result in fitting the training data and lead to overfitting. Moreover, the system has poor generalization and has not been evaluated on different sign language datasets having high variation, and the training time has not been measured.

The problem of poor generalization was the main interest in [ 27 ], and [ 28 ], in which both of the static and dynamic Indian sign language has been recognized in [ 27 ] by selecting the optimal features set and upgrade the related wights in the Multilayer Perceptron (MLP) model depending on the use of a hybrid metaheuristic algorithm so called Deer Hunting-based Grey Wolf Optimization (DH-GWO). The statistical results for static Indian sign language were 97%, 94.6%, 81%, in term of Accuracy, Precision, and F1 score while the results for the dynamic dataset were equal to 89%, 47%, 30%. Although the findings were impressive for static sign language, this effort requires a significant amount of time and has a high level of computing complexity. Furthermore, the dataset being used is not accessible to the public. While in [ 28 ], two types of static sign languages have been recognized including Mexican of 99.37% accuracy and American that show 99.98%. The parameters of the Deep Convolutional Neural Networks (DCNN) have been tuned using the Particle Swarm Optimization (PSO) in this work.

Despite the high recognition results, significant computer resources may be necessary to handle huge datasets or complex designs due to the PSO require a computationally intensive task of training several candidate networks to assess the fitness of each particle.

Selecting the optimal features for sign language recognition was the goal in [ 29 ], in which the features are extracted and a metaheuristic PSO algorithm is used before the classification. The selected features afterward are classified using the multi-class Support Vector Machine to avoid the high computational complexity in deep learning. It has been evaluated on seven public datasets for three different sign languages having uniform and complicated backgrounds including Indian dataset, Jochen Triesch (JTD), MNIST dataset, NUS Dataset II, Static Hand Posture with different background, Arabic dataset IEEE ASL dataset of accuracies equal to 99.18%, 93.07%, 90.9%, 96.7%, 91.1%, 93%, 80.1% in sequence. Notwithstanding its commendable outcomes and endeavors to surmount challenges related to generalization and computational expense, this system has been noted to exhibit difficulties with dynamic signs and a notable propensity for producing errors in certain letters, including J and Z. Reducing the feature set will, nevertheless, impact precision and must invariably involve a trade-off. Furthermore, neither the necessary time nor dynamic sign languages were accounted for in the evaluation.

Recognizing the complex Arabic sign language is the main interest in [ 30 ]. The optimized Deep Convolutional Autoencoder by the Atom Search Optimization algorithm has been utilized for classifying the extracted features using the capsule network (CapsNet). The statistical results were measured using Accuracy, Precision, Recall, and F-measure equal to 99.17%, 95.52%, 95.45%, 95.31%. The utilized dataset in this work is not big enough to prove that the system will have the ability to recognize all the Arabic words, and the needed time is not mentioned.

In [ 31 ], the gesture has been recognized using hyperparameters tuned CNN by the newly developed Harris Hawks Optimization (HHO) algorithm. The recognition accuracy was equal to 100% and the required training time is 15 min. While the presented system demonstrates a commendable rate of recognition, it fails to account for generalization and fails to specifically tackle the demands of real-time applications that prioritize rapidity and computational effectiveness.

To overcome the high dimensionality in the features of sign language, some related works employ the deep learning to extract the features in [ 32 , 33 , 34 , 35 ]. The MobileNet model has been employed for features extraction in [ 32 , 33 ]. Moreover, the Artificial Rabbits Optimizer is used for tuning the hyperparameters of Siamese Neural Network in [ 32 ] for recognizing sign language and shows a 99.14% accuracy.

While in [ 33 ], the parameters tuning has been done on both MobileNet, using the Manta Ray Foraging Optimization (MRFO), and Hybrid Deep Learning model using the Reptile Search Algorithm. The alphabetics of the American sign language has been recognized with a 99.51% accuracy.

The connected densely network (DenseNet169) for extraction features in [ 34 ] has been used, the parameters of the Multilayer Perceptron (MLP) models are optimized using the Deer Hunting Optimization algorithm of 92.88%. recognition accuracy for the Arabic sign language.

On the other hand, in [ 35 ], the model of Inception v3 is used to generate the features map and the Deep Wavelet Autoencoder is fine-tuned using the metaheuristic algorithm “Sand Cat Swarm Optimizer” to recognize the American sign language alphabets and number and show an accuracy rate for recognition equal to 99.01%. These four studies have complexity, time, and poor generalization since they were not tested on numerous datasets with several changes and cannot be applied in the real-world fields.

In [ 36 ], the parameters of a developed CNN are tuned using three metaheuristic algorithms including Whale Optimization Algorithm (WOA), Particle Swarm Optimization, and an Improved Competitive Gray Wolf Optimizer (ICGWO) and show the recognition accuracy rate on English alphabets in Indian Sign Language (ISL) equal to 99.93%. This developed (CNN) exclusively processes the input image of the hand, as opposed to the entire body. This necessitates precise hand detection or segmentation. Furthermore, the architecture is computationally demanding, requiring iterations based on the number of solutions. Consequently, it can be laborious and time-consuming, particularly when dealing with extensive datasets or intricate CNN architectures.

While researchers have utilized various metaheuristic algorithms to optimize features and tune parameters in models of machine learning for sign language or gesture recognition, little emphasis has been placed on addressing issues related to time, complexities, computing power, and generalization.

Given the issues mentioned earlier a developed metaheuristic optimization algorithm called the WAR Strategy has been utilized to optimize features and fine tune hyperparameters of machine learning models, for sign language recognition, in this paper. The key aspects of the system outlined in this paper are as follows:

Optimize the extracted features from the sign language images to overcome the problems related to hand gestures variations using the metaheuristic WAR Strategy algorithm.

Fine-tune the hyperparameters of six machine learning models using the metaheuristic algorithm (WAR Strategy) to accurately identify sign language all while reducing expenses and time.

Assess the efficacy of the proposed system and evaluate its high generalization on three publicly accessible sign language datasets of significant variances, namely American, Arabic, and Malaysian.

3 WAR Strategy Optimization Algorithm

The emergence of a collection of optimization algorithms so called Metaheuristic is a result of the raise difficult and complicated problems in numerous fields and applications in the current period, that cannot be handled within an acceptable timescale and at a reasonable computing expenditure. In situations when more conventional optimization techniques have failed, these algorithms, which draw their inspiration from social or natural phenomena, may frequently provide effective and adaptable solutions. In order to get the most out of systems, metaheuristic optimization algorithms seek for optimum or nearly optimal solutions within a given problem area. Numerous fields have found their way into its implementation, such as scheduling, routing, hyperparameter tweaking in machine learning, feature optimization and selection, and many more [ 37 ]. To solve optimization issues, a newly developed metaheuristic algorithm called the WAR Strategy Optimization Algorithm emerged. Its primary source of inspiration is the strategic planning of the military forces that go into battle. Each soldier autonomously converges towards the optimal value. This optimization algorithm incorporates two well-known approaches to combat, namely offensive and defensive tactics. The positions of the troops on the battlefield are modified in line with the plan that is put into action. A new method for updating weights and a plan to move weak troops are used to make the algorithm more resilient and improve its convergence. The proposed war strategy algorithm shows a quick convergence speed across different search areas and does a good job of balancing the exploration and exploitation phases [ 38 ]. The main steps of the WAR Strategy optimization algorithm are illustrated in Algorithm 1 [ 38 ].

Algorithm 1: The Metaheuristic WAR Strategy Optimizer.

4 Sign Language Proposed System Methodology

The sign language recognition system described in this work comprises a series of stages and procedures, each of which plays a crucial role in accomplishing the desired goals. As shown in Fig.  1 , before beginning the system procedures, the data is divided into two separate groups, with 70% given for training the system and the remaining 30% for testing and evaluation. The first stage of the proposed system consists of a series of pre-processing procedures. Afterwards, the identification and division of the area of interest is performed in order to prepare the data for the subsequent stage. The subsequent stage involves feature extraction, which depends on the employment of two techniques: Linear Discriminant Analysis (LDA) and Gray-level cooccurrence matrix (GLCM), to provide a comprehensive collection of features that accurately represent the data. The feature optimization phase follows, with the goal of obtaining the best collection of features from the ones collected using LDA by using the WAR Strategy method is then implemented. Subsequently, the WAR Strategy algorithm has been adopted to fine-tune the hyperparameters of six (ML) algorithms, resulting in developing a new classifier to be used in the final phase.

Architectural model of proposed static sign language recognition system

4.1 Sign Images Preprocessing

The sign language dataset may exhibit several changes, which can be attributed to factors such as the capturing instrument, environmental conditions, orientation, occlusion, position, and so on. The primary objective of preprocessing prior to classification using ML is to enhance the quality of the data in order to produce efficient and accurate classification results. Preprocessing techniques are used to address these kinds of variance, such as in the elimination of noise, improvement of contrast, normalizing, and other related methods.

The proposed system includes a series of preprocessing steps as follows.

Convert the colored RGB sign language images into greyscale which exhibits a high effect on forwarded phases mainly on the extraction of features. Whereas the greyscale images include just one-color channel, ensuring that the retrieved features remain unaffected by color. Therefore, it is a crucial process in several applications as it decreases computational complexity, lowers processing times, and simplifies the execution of afterwards steps [ 39 ].

Contrast adjustments using the Histogram Equalization technique, due to incorrect distribution of lights or insufficient illumination may lead to misclassification [ 40 ].

Apply the Gaussian Blur filtering to decrease the high-frequency components of an image in order to eliminate noise and enhance features, particularly those associated with edges and lines. Equation ( 1 ) describes the mathematical equation of the used Gaussian filter, that applied using a 3 × 3 kernel size in this system, which will provide a smoothed image with the needed features. [ 41 ].

Convert the image to binary colour in order to simplify the process and make it easier to identify the most crucial areas that contain vital details. Binarization enhances the capability to differentiate the foreground and background of an image [ 42 ]. Thresholding based binarization method has been implemented in this system with a threshold value of 115.

4.2 Cropping and Resizing

It is essential to use segmentation techniques to isolate the precise area of the hand involved in sign language from the given image. It is important to apply a precise identification procedure since it greatly influences the outcome. This research utilizes the well-recognized contour-based segmentation approach, specifically known as edge-based segmentation, to collect and segment the Region of Interest (ROI) in the image [ 43 ]. The first part of this technique involves identifying the edges, followed by a connecting step that utilizes the continuity of curved lines and edges to depict the hand area and form in the image [ 44 ]. The Contour-based segmentation approach has been used, depending on the presence of the axis in the binary image of lined edges, to extract the hand portion from the smoothed grayscale image.

Afterwards, the segmented image of the hand area will be resized to enhance its finer characteristics and reduce processing time. The suggested system utilizes the widely used Bilinear Interpolation Scaling technique for resizing. This technique uses the quartet of adjacent pixels to calculate the desired pixel and functions in both the vertical and horizontal orientations [ 45 ]. In this work, the sign language images have been resized to dimensions of 50 × 50.

4.3 Feature Extraction

Irrelevant characteristics may lead to incorrect categorization and erroneous recognition when it comes to sign language recognition, thus extracting the relevant features is crucial for getting accurate results [ 46 ]. With the goal of improving the efficiency of data analysis and storage while decreasing processing time and shedding light on complexity, feature extraction has a strong impact on the problem of finding the most succinct and meaningful set of characteristics. This kind of data representation for classification and regression is therefore often regarded as the most popular and practical option.

Two feature extraction methods have been employed in this paper: Linear Discriminant Analysis (LDA), and Gray-Level Co-occurrence Matrix (GLCM), to acquire spatial and temporal features of the sign language image dataset.

The Linear Discriminant Analysis (LDA), commonly known as Fisher’s Discriminant Analysis, is a widely used method for reducing dimensionality and extracting desired features. The majority of its employment is in supervised classification situations [ 47 ]. The approach works by determining the most effective linear combinations of characteristics that accurately differentiate between different groups or categories. The number of characteristics it gives is equal to the number of classes minus one, which is beneficial for dealing with the multiple-class issue [ 48 ]. On the other hand, the GLCM is a square matrix is used to express the odds of pairs of pixel intensities occurring together at a certain distance and angle inside a picture [ 49 ]. It has been used to extract the relevant texture characteristics in the picture, using diverse statistical measures of the pixels. Six features are acquired using the GLCM: Inverse Difference Moment, Energy, Entropy, Homogeneity, Mean, and Contrast [ 50 ].

4.4 Features Optimization by Metaheuristic Algorithm (WAR Strategy)

It is vital to get the optimal or nearly optimal collection of features that accurately reflect the most important information, including all relevant aspects, of sign language in various poses and environments. This work utilizes the metaheuristic algorithm (WAR Strategy) to optimize the features set obtained from employing the LDA and GLCM feature extraction methods. The key parameter values used in the WAR Strategy method for optimization of the features are shown in Table  1 .

The Easom function is a single-modal test function known for its global minimum, which is located in a very small region compared to the total search space. It has just two variables and is often used as a benchmark for evaluating the effectiveness of optimization strategies. The equation for the Easom function is as follow [ 51 ]:

Test area is usually restricted to square \(- 100 \le d_1 \le 100, - 100 \le d_2 \le 100\) . Its global minimum equal f ( d ) = – 1 is attainable for \(\left( {d_1 ,d_2 } \right) = \left( {\pi ,\pi } \right)\) .

4.5 Tuned Machine Learning Models-Based Sign Language Classification

Bio-inspired algorithms are renowned for their effective use in hybrid approaches to fine-tuning the parameters of ML models. Hyperparameter fine-tuning is the process of systematically optimizing the hyperparameters of a ML model to improve its overall performance and enhance its efficiency by determining the optimal values of these parameters that makes these models reaches the desired stability range and achieve its main goal [ 52 ]. The hyperparameters of six well-known ML models are fine-tuned using the WAR Strategy optimization algorithm. These machine learning models are Support Vector Machine (SVM) [ 53 ], Random Forest [ 54 ], Logistic Regression [ 55 ], Decision Tree [ 56 ], K-Nearest-Neighbors (KNN) [ 57 ], and Naïve Bayes [ 58 ]. The WAR Strategy optimization algorithm will enhance the performance of these ML models by optimizing their most effective hyperparameters. The improved versions of these models will then be used to classify the extracted and optimized features of the sign language for the purpose of recognition. The specific parameters that will be adjusted for each ML model are listed in Table  2 , while Table  3 , illustrate the parameters of the WAR Strategy optimization utilized in this hyperparameters optimization phase.

Ackley’s is a test function of type multimodal and can be defined as follow [ 59 ]:

The area of testing is mainly bounded to the hypercube \(- {32}.{768 } \le x_i \le { 32}:{768},{\text{ i }} = { 1,} \cdots , {\text{n}}\) , while the global minimum f (x) = 0, that is achieved for \(x_i = \, 0,{\text{ i }} = { 1}, \cdots ,{\text{ n}}.\)

The tuning for these hyperparameters will be done in this paper using the WAR Strategy algorithm. The initial values of these hyperparameters in of the employed ML models and the values obtained after fine-tuning them using the WAR Strategy algorithm are shown in Table  4 . The obtained hyperparameter values will be used in the optimized version of the ML models in the proposed system.

It is noted from Table  4 that the optimal values of the hyperparameter for all ML models are obtained by employing the WAR Strategy algorithm. Figure  2 exhibits the WAR Strategy optimization algorithm during the fine-tuning of these parameters.

The diffusion behavior of the WAR Strategy Algorithm during fin-tuning the ML hyperparameters

5 Experimental Evaluation and Results

The proposed sign language recognition system was assessed for its ability to recognize static signs in three benchmark datasets representing three different languages: American, Arabic, and Malaysian. The efficacy of the presented system, which utilizes the optimized version of the ML models has been demonstrated in handling the numerous variations present in the three datasets used, including differences in lighting conditions, distance, backgrounds, dimensions, positions, and orientations. Moreover, it displays exceptional accuracy of recognition while decreasing the training time, especially when dealing with several images of different quality. The effect of using the WAR Strategy optimization algorithm in both feature optimization phase and hyperparameters tuning has been assessed. in which, the sign language has been recognized first without using the WAR Strategy algorithm, second using it only for optimizing the features, and third employ it both phases (optimizing the features and ML parameters) which is the proposed system.

The utilized performance measurements used to assess the efficiency of the presented system are accuracy, precision, F-measure, recall, and training time of the model.

The implementation environment of the presented system includes a laptop of type ASUS having AMD Ryzen 9 5900HS with Radeon Graphics 3.30 GHz, RAM 16 GB size, and an NVIDIA GeForce RTX, of operating system type Windows 10 of 64-bit.

5.1 American Sign Language Dataset Results

It is a static sign language dataset describing the letters of American language, acquired from Kaggle [ 60 ]. It contains an 87,000 colored sign language image stored in a JPG file format of a 200 × 200-resolution. all these images are stored in 29 folder or class, in which 26 assigned for the American letters from A-Z, and the rest are assigned for the DELETE, NOTHING, and SPACE. All the images in this dataset suffer from a high level of variations and gathered in variant lighting and locations in many backgrounds. Figure  3 shows some samples of this dataset, while the WAR Strategy algorithm diffusion for optimizing the features of this dataset is shown in Fig.  4 . The statistical results of this dataset acquired from the two case studies and the proposed system are presented in Tables 5 , 6 , and 7 .

American sign language dataset samples

The diffusion of the WAR Strategy algorithm on the American sign language dataset features

The statistical findings of the three case studies indicate that the suggested system has attained the maximum recognition accuracy via the use of the WAR Strategy optimization algorithm. Four fine-tuned ML models have reached a perfect accuracy rate of 100% including decision tree, random forest, SVM, and KNN. This refers to the effectiveness of the system in dealing with the many variances in sign language, with a training time of just 0.1 s obtained from the decision tree. Figure  5 displays a comparative flowchart describing both accuracies and the required time for training acquired from the proposed system and the previously examined cases of study.

The proposed system results compared with the two cases of study for recognizing the American sign language

5.2 Arabic Sign Language Dataset Results

The Arabic sign language dataset utilized in this work is the (ArSL2018), which is large and labelled [ 61 ]. There is a 54,049 static Arabic sign language image in this dataset for the 32 Arabic alphabets stored in greyscale format of 64 × 64 resolution. It has been gathered using 40 participants of many ages using a smart camera in a single position, 1 m far away. Some of these images of Arabic sign language of this dataset is presented in Fig.  6 , which it is clear that it suffers from many variations, while the WAR Strategy algorithm diffusion for optimizing the features of this dataset is shown in  Fig. 7 . Tables 8 , 9 , and 10 , exhibit the statistical results of this dataset.

Samples of Arabic Alphabetic Sign Language Dataset

The diffusion of the WAR Strategy optimization algorithm on the Arabic sign language dataset features

Despite the complex nature of Arabic sign language and the poor lighting and low resolution of the dataset used, the proposed system has demonstrated its effectiveness in addressing such limitations. Many tuned classifiers achieved 100% recognition accuracy with minimal training time. Figure  8 clearly illustrates a direct comparison between the proposed method and the two cases of study including without using the WAR strategy and use it only for feature optimization.

The proposed system results compared with the two cases of study for recognizing the Arabic sign language

5.3 Malaysian Sign Language Dataset Results

The Malaysian sign language utilized in this paper contains a clear and focused images of a hand gesture corresponding to a Malaysian letter, and numbers attained from Kaggle [ 62 ]. This dataset that alphabets, numbers, and words. The evaluation of the presented system was made on 12,400 images of the Malaysian alphabets and numbers only. All the images are jpg colored of 640 × 480 resolution. The images representing the numbers are distributed scattered in 11 folders, and each one has 300 images. While the images representing the alphabet are scattered in 26 folders, in each one are 350 images for each letter. Samples of this dataset is presented in Fig.  9 , and the obtained results from implementing the system without using the WAR strategy optimization algorithm, and when use it for feature optimization and the proposed system are illustrated in Tables  11 , 12 , and 13 , sequentially. Moreover Fig. 10 , illustrate the diffusion of the WAR Strategy algorithm during optimization of the features extracted from this dataset.

Malaysian alphabet sign language dataset samples

The diffusion of the WAR Strategy optimization algorithm on the Malaysian sign language dataset features

Although the Malaysian sign language dataset is extensive and has many dimensions, the approach used in this study achieves good accuracy in recognizing signs by employing many optimized classifiers. Figure  11 is a flowchart that demonstrates the accuracy and duration of training achieved by the proposed system in comparison to the two cases of study.

The proposed system results compared with the two cases of study for recognizing the Malaysian sign language

5.4 Discussion

The metaheuristic algorithm (WAR Strategy) efficiency has been proved from the obtained result analysis and can be considered as a powerful optimization algorithm due to it having a rapid convergence and avoids getting trapped in the local optima. whereas, in the beginning it become very clear when employing it for optimizing the features, which raised up the superiority of the proposed system in overcoming the problems of sign language recognition, such as variations in illumination, poor resolution, orientation, occlusion, and poses. In addition, when using the WAR Strategy in optimizing the hyperparameters of the ML models, has raised up the accuracy of recognition of sign language in American, Arabic, and Malaysian, has achieved a maximum of 100% from applying the tuned version of ML models. Moreover, in nearly all of the optimized classifiers, the time necessary to train the ML models has been decreased to below one second. By implementing the suggested system on three unique sign language datasets characterized by different attributes, the issue of generalization in sign language recognition systems had been addressed. The outcomes were both time-efficient and accurate. A substantial disparity will become apparent when contrasting the outcomes of utilizing conventional machine learning models in the context of sign language recognition with the tuned version of these models. By analyzing the results obtained from the second case study, the efficacy of the WAR Strategy algorithm was initially observed when it was applied to feature optimization. Furthermore, the results obtained from the proposed sign language recognition system unequivocally demonstrated and documented the efficacy of employing the WAR Strategy algorithm for feature optimization and machine learning hyperparameter tuning. Earlier-conducted case studies and the evaluation of six machine learning classifiers against three distinct categories of sign language datasets collectively indicate that the WAR Strategy algorithm is quite effective. Moreover, due to its remarkable performance, the proposed system obviates the need for time-consuming and intricate deep learning methodologies, as it instead utilizes fine-tuned classical machine learning models to recognize sign language accurately and rapidly.

To establish the clear advantage of the proposed sign language recognition system over previous research, a comparative analysis was undertaken. The findings of this examination are detailed in Table  14 . When compared to previous systems, the proposed method demonstrates the lowest execution time (0.1 s) and the highest identification rate (100%). The accuracy rate and the required training time outcomes for each tuned classifier across the three datasets are illustrated in Fig.  12 .

The results of the proposed system on the three examined datasets

6 Conclusions and Future Work

This paper presents an efficient and accurate recognition system for static sign language in American, Arabic, and Malaysian that rely on employing the recently developed metaheuristic WAR Strategy algorithm. After processing the sign language image by correcting its contrast and cropping the desired hand part from it and extracting the spatial and temporal features using two powerful techniques (i.e. LDA, GLCM), the WAR Strategy algorithm have been applied on the extracted features set. Whereas, by applying this metaheuristic optimization algorithm will result in producing an optimized features set that holds the most significant information of the signs in the sign language. The features optimization phase makes the proposed system able to overcome many obstacles raised when recognizing the sign language such as variance in lighting, difference in orientations and poses, and partial or complete occlusion. Moreover, the WAR Strategy optimization algorithm has been applied a second time to optimize the hyperparameters of six widely utilized Machine Learning models. Tuning the machine learning models' parameters has extremely improved the performance of the proposed system, as observed in the previously presented recognition results which exhibit a 100% accuracy rate. As well as this parameters optimization has improved the ML processes and reduces the required time for training the model that even reached to 0.5 s in some of the tuned versions of ML models. As a result, the proposed system proves its efficiency in dealing with large data sets that have many variations and show a high performance. in addition, the proposed system has proven its high generalization by recognizing different sign languages (i.e. American, Arabic, Malaysian) and give a superior result on all of them. In the future, the intention is to enhance the system to be applied on dynamic sign language or test the proposed system on a different kind of data such as sound.

Data Availability Statement

All the datasets that have been adopted in our experiments are publicly available and can be accessed as follow: ASL Alphabet Dataset available for public on Kaggle and can be accessed via the following link: https://www.kaggle.com/datasets/grassknoted/asl-alphabet?resource=download . The DOI for the dataset is https://doi.org/10.34740/kaggle/dsv/29550 . The dataset was accessed on October 15, 2023. ArASL: Arabic Alphabets Sign Language Dataset available for public on Mendeley Data and can be accessed via the following link: https://data.mendeley.com/datasets/y7pckrw6z2/1 . The DOI for the dataset is https://doi.org/10.17632/y7pckrw6z2.1 . The dataset was accessed on October 30, 2023. Malaysian Sign Language (MSL) Image Datase available for public on Kaggle and can be accessed via the following link: https://www.kaggle.com/datasets/pradeepisawasan/malaysian-sign-language-msl-image-dataset . The DOI for the dataset is https://doi.org/10.34740/kaggle/dsv/7135047 . The dataset was accessed on November 4, 2023.

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Abd Al-Latief, S.T., Yussof, S., Ahmad, A. et al. Instant Sign Language Recognition by WAR Strategy Algorithm Based Tuned Machine Learning. Int J Netw Distrib Comput (2024). https://doi.org/10.1007/s44227-024-00039-8

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Detail from a Minoan fresco fragment ( c 1450-1400 BCE), excavated at Knossos. Courtesy the Ashmolean Museum , Oxford

Laughing shores

Sailors, exiles, merchants and philosophers: how the ancient greeks played with language to express a seaborne imagination.

by Giordano Lipari   + BIO

The shore: that immeasurable fringe that will exist as long as sea and land do. At waterfronts, we relish gently splashing waters at the edge of glassy expanses, where powerful currents move unfathomable volumes at speed. At waterfronts, we dread waves stirred by the wind and coming at an inexorable beat from invisible distances.

The shore has always evoked deep emotions of detachment, exposure and homecoming for our terrestrial selves seeking at sea adventure, betterment, power, profit and sustenance. No surprise that the arts, science, technology and accounts of everyday endeavours have devoted to it words and numbers beyond survey. Here, we turn to some literature come down to us from Greek antiquity and to a little physics held in common with those predecessors. Scrolling documents that survived history will tell us of some who feared shipwrecks and longed for a safe return; and of others who wondered how waves deliver the motion harvested from the wind to the land. In the shore we will recognise a fascination for instability in deed and in metaphor; the quests for shelter from harm and for sound knowledge; and perpetual laughter that language has forgotten.

Searching for connections that could still be with us across the ages is a fourfold challenge, however. Ahead lie words liable to uncertainty of provenance; meanings of a disappeared language; meanings that modern minds might reluctantly embrace in mismatching words; and a world of events that has kept on boggling minds ever since.

Dear reader, fear not. The occasional text in Greek script, always in parentheses, can be passed over. The translations are few and my own. Stock physics primarily conveys the belief that rules of nature have stood while humankind has been making sense of it one way or another. Would you dissent from that belief, then what remains will speak of the unchanged drive to navigate the world around us and decide what to do with it.

Who would run voluntarily across so endlessly much salty water? ( τίς δ’ ἂν ἑκὼν τοσσόνδε διαδράμοι ἁλμυρὸν ὕδωρ ἄσπετον ) – from the Odyssey

L et’s first do a fast rewind of 2,800 years or more. No hearing or eyesight support for the elderly, no wristwatches, no thermometers, no electricity, no engines, no plastics, no paracetamol, no three-day weather forecast – and many more such privations for us moderns. Iron was to rise above bronze, though. And, in the teeth of technological poverty, memories were by and large better trained than ours. Let’s look at Odysseus’ celebrated homecoming.

Photo by Jens Aber/ Unsplash

Being a collective work, the verses of the Odyssey kept alive in the oral tradition much know-how and many cautionary tales about surviving in the world. Most of its fifth book is about sailing heavy seas and making landfall safely. Six marine scenes glimpse the stakes of sailing away and back as that culture felt them. As happens between Poseidon and Odysseus, the lord of the seas hates a sailor.

On Zeus’ injunction, the nymph Calypso reluctantly lets a homesick Odysseus go. She provides him with prime raw materials to build a raft, sustenance and fine clothes for the journey, some loving parting, and a fair tailwind. Odysseus equips his raft craftily with plentiful features, including a railing of shrubs to fence off the wave splash. Wide awake at sea for a row of uneventful days, he eventually makes sight of land. And all hell on the water breaks loose.

Odysseus spots land on the horizon once again from the crest of a helping wave

The wayward immortal gets at the homesick mortal. Poseidon, just returned from a leisurely stay in Africa, is angered at having been sidestepped when his fellow gods cleared Odysseus’ homeward route. He excites winds and waves from all quarters, generating what we would call a multiple cross sea, a serious difficulty even for modern vessels. A few big waves impart mighty blows. A garlanded warrior, Odysseus dourly rates as despicable the death by drowning decreed upon him ( νῦν δέ με λευγαλέῳ θανάτῳ εἵμαρτο ἁλῶναι ).

There is benevolence in the divine, though. Moved by pity, the sea-goddess Ino offers Odysseus a sacred cloth that will protect him from fatigue and fear like a magical life jacket. Conditions apply: he must undress first, jump overboard and return the cloth once safe on dry land. After another wave shatters Odysseus’ raft and reluctance, the swimming begins. Conveniently, Poseidon withdraws, appeased by so much of Odysseus’ distress. Athena, Odysseus’ patroness, gives a forward direction to the waves by shutting down all winds but one: ‘she stirred up the stiff northern wind and the waves broke ahead’ ( ὦρσε δ’ ἐπὶ κραιπνὸν βορέην, πρὸ δ ὲ κύματ’ ἔαξεν ). The wind eventually settles and Odysseus spots land on the horizon once again from the crest of a helping wave. At last the storm is over, but things are not going swimmingly yet.

The nearing of the shore is the next predicament. Odysseus hears the roar of the waves crashing on a jagged promontory. To no avail does he seek ways to set foot on dry land ( νῆχε δ’ ἐπειγόμενος ποσὶν ἠπείρου ἐπιβῆναι ) and wade out of the grey sea ( ἔκβασις οὔ πῃ φαίνεθ’ ἁλὸς πολιοῖο θύραζε ). The seabed around the cliffs is too deep to stand on both feet ( οὔ πως ἔστι πόδεσσι | στήμεναι ἀμφοτέροισι καὶ ἐκφυγέειν κακότητα ). Specifically, he longs for some approach that is harboured and stricken by waves either parallel to the shore or aslant, the translation depending on subtleties that will challenge us later ( ἤν που ἐφεύρω | ἠϊόνας τε παραπλῆγας λιμένας τε θαλάσσης ). While he ponders his odds against the cliffs and against Poseidon’s ocean-riding monsters and storms, a treacherous billow dashes him against a rock. He manages to cling to it but the backwash drags him harshly to the open sea again.

Odysseus starts over. He swims out along the shore to avoid being snatched by the billows again ( νῆχε παρέξ, ἐς γαῖαν ὁρώμενος, εἴ που ἐφεύροι | ἠϊόνας τε παραπλῆγας λιμένας τε θαλάσσης ). He then senses the stream of a river, and the approach looks free from rocks and is sheltered from the wind. His prayer for admittance to safety is heard by the river-god:

As Odysseus spoke, the river immediately stopped its own current, held the wave, made the sea ahead calm for him, and saved him into the out-flowing river. ( ὣς φάθ’, ὁ δ’ αὐτίκα παῦσεν ἑὸν ῥόον, ἔσχε δὲ κῦμα, | πρόσθε δέ οἱ ποίησε γαλήνην, τὸν δ’ ἐσάωσεν | ἐς ποταμοῦ προχοάς. )

So Odysseus swims into the river mouth without overpowering its stream, an exhausting effort, similar to swimming against a rip current.

At long last, naked, battered, bruised, swollen and gasping, Odysseus crawls on all fours onto dry land. He duly returns Ino’s cloth by dropping it into the river, and kisses the ground. Only after finding cover from the nightly chill and wild beasts can he fall asleep with the blessing of Athena. While his odyssey continues for 19 more books, we stay by the word choices of Book 5 in search of insights into seas and shores.

S everal words denoted the sea. Thálassa ( θάλασσα ) was the sea both generically as well as, prosaically, the water bunging up one’s airways. Pélagos ( πέλαγος ) and póntos ( πόντος ) were specifically the open high sea. Háls (salt, ἅλς ) and hygrá (the moist, ὑγρά ) were generic names by association. The sea of Odyssey 5 is barren ( atrýgetos , ἀτρύγετος ) but also abounding in fish ( ichthyóeis , ἰχθυόεις ); divine ( dîos , δῖος ); frightening and tormenting ( deinós , δεινός ; argaléos , ἀργαλέος ); grey, or perhaps hoary owing to foam streaks ( poliós , πολιός ); hazy ( eeroeidés , ἠεροειδής ); violet-hued ( ioeidés , ἰοειδής ); and, famously, looking like wine ( oînops , οἶνοψ ). The colours of the sea in ancient Greece have been an Aeon essay of their own.

A single word denoted ‘waves’. The term kŷma (κῦμα) was associated with a broad notion of swelling that included pregnancy. Apart from ripples, the ancient Greek language did not distinguish the shorter, slower and steeper waves directly generated by the wind, and the longer, faster and milder ones that travel away from the area of generation – the modern’s ‘swell’ proper. The waves of Odyssey 5 are most often big ( mégas , μέγας ), and also black ( mélas , μέλας ), solid ( pegós , πηγός ) and tall ( makrós , μακρός ). The overturning of waves has a place in the verses when the gale makes them roll over ( μέγα κῦμα κυλίνδων ) and when an overhanging wave ( katerephés , κατηρεφής ) shatters Odysseus’ raft.

And several nouns denote the sea’s great counterpart, the land. Specifically, eión ( ἠϊών ) was the shore, the longed-for threshold of precariousness. Odysseus at sea understood very well that his survival amounted to an exit to walk on. What he wanted stands in a two-for-one figure of speech that pairs a shore hit somehow by the waves ( ἠϊόνας τε παραπλῆγας ) and a sheltered sea ( λιμένας τε θαλάσσης ). There, though, the translation runs into a stumbling block or a stepping stone.

The very verses validate that favourable waves make landfall parallel to the shoreline

The compound structure of the modifier paraplêgas ( παραπλῆγας ) hides which circumstances made shores ( eiónas , ἠϊόνας ) favourable to landfall. Its second part, plêgas , relates to fits of hitting and striking, which waves do. Its first part is the preposition/adverb pará ( παρά ). This expressed side-to-side arrangements of things with a variety that is ambiguous for our purposes. On the one hand, pará could indicate objects keeping that arrangement in space or time – think of the senses of ‘along’ and ‘abreast’ and of the loanword ‘parallel’. On the other hand, it could stress the coming to or parting from it – think of ‘across’ and ‘aslant’; for example, in ancient Greek, both paránoia ( παράνοια ) and paraplexía ( παραπληξία ) meant madness through that idea of getting ‘off one’s rocker/trolley’. Back to the shore, did the Odyssey inform us that safer waves approached the shore frontally and parallel or, rather, obliquely and aslant? The venerable Liddell-Scott-Jones dictionary tells us ‘aslant’.

A sure thing is the need to survive a danger fundamentally unchanged across the ages. As we all would, Odysseus stayed clear of the sudden breakers that had just dashed him on a rock. He started over and swam on, proceeding along and out, paréx ( παρέξ ), as anyone should. And since paréx is a compound word featuring pará , in all likelihood the coastal pará meant ‘along’ rather than ‘aslant’. So, never mind the dictionary, within so close a reach the very verses validate that favourable waves make landfall parallel to the shoreline.

On second thought, though, Odysseus also longed for the sheltered waters of a harbour, the second piece in the two-for-one figure of speech above ( λιμένας τε θαλάσσης ). And something must shelter that harbour, perhaps the promontory whose rocks Odysseus just escaped. And a headland shelters a nearby shore from waves rolling into it aslant more likely than frontally. So, slanted waves too hint at safety, consistently with the scenery, idealised though it can be. The ‘aslant’ sense of pará is credible too.

On a third pass of thinking, terrestrial Odysseus insisted on walking ashore. As a matter of fact, disordered waves in deep water are often seen to break parallel to a shore, and a seabed sloping landwards is the clever operator there. In a natural process called refraction, the advance of waves slows down in waters that grow shallower. This slowing down bends towards the shore whatever direction the waves had in deeper waters. As a matter of lore, from that change of wavescape any sea-savvy person could make out the formless boundary telling a nearshore approach from the high sea, póntos/pélagos , traversed by waves from any quarter. In effect, this process of bending starts when the water oscillating below the wave surface grazes the seabed. That, in turn, begs us to appreciate as a rule of fact that, beneath the evident bobbing and rolling, the water motion vanishes deep down enough. Regardless of our having in hand a validated theory for wave refraction, a gradually sloping shore does give a reassuring measure of organisation to the waves coming from far off. The capricious billows slapping the rocks in deep waters featured in the verses as the contrary to that gentler way out. So, did the perplexing paraplêgas hint at a walkable broad zone of surf? Why not! The ‘along’ sense of pará sounds credible once again.

For the ancient Greeks, the verses that puzzle us had either an ambiguity they lived with or no ambiguity

The single word paraplêgas got us locked in a corner case. The entries of the dictionaries did not reassure us firmly that ‘X stands for Y rather than Z’. At face value, the ambiguity of the little word pará seemed to entail a small change of interpretation. Yet, this small change called for extensive elaborations on the greater scenery. Contextualisation, checks of linguistic consistency and physics-inspired inference did not confirm ‘from X follows Y rather than Z’. There is no bad guy either: neither translation impairs the coherence of the narrative and realistic expectations. There is not even a penalty for leaving things indistinct: since waves with a slanted direction of approach may hit a shore all along, melding both interpretations of pará paints a realistic picture. We give in to indifference, for linguistic ambiguity has morphed into undecidability.

For all the timeless rolling of waves, a concrete risk of overthinking lurks in the commitment to the textual evidence from 2,800 years back. For the ancient Greeks, the verses that puzzle us had either an ambiguity they lived with or no ambiguity. In the former case, it is sufficient to pinpoint where that ambiguity stood, and let it be. Our attempt at fixing distinctions with a difference has played out against the odds of the latter case, actually. In seeking special circumstances in which waves could aid landfall, we tried the odds of translating a defunct language and stumbled on half a word. So, let’s be content to savour the timelessness of seas and shores like the ancient singer-storytellers, at a small loss of clarity. There was a shore, there were waves pounding that shore.

Conceivably, the verses of the Odyssey had evolved for centuries by slow degrees until any cognitive dissonance with everyday experiences was felt to be unimportant. Survivors could have contributed their stories as swimmers, fishers, merchants, exiles and warriors, while untaught hearers could have been forewarned about the dangers at sea. Sailing is a life-or-death thing. If the epic in the Odyssey served as a primer to cope with the unexpected, then the guidance for those in distress at sea could have been: save your skin doing as the hero Odysseus did. When sailing in cross seas, keep your wits about you and pray that it ends before you end up drowned. When swimming, keep out and along the shore, keep clear of rocks, do not swim against a streaming river, spot sheltered waters first; an unsheltered shore will do you better than cliffs. Expect fatigue and fear. Kiss the ground if you make it. You’ll be frail, don’t lower your guard in apparent safety. Be thankful that some gods have protected you. That awe at having made it alive ashore, too, could be fundamentally unchanged across the ages, to the memory of those lost at sea.

And we ran to the sea On the journey on the sea Too many passengers died They got lost to the sea A boat was carrying 90 passengers Only 30 were rescued And the rest died Today we are alive The sea is not a place to pass by The sea is not a road Oh, but today we’re alive

– from Gianfranco Rosi, Fuocoammare

Photo by Bert B/ Unsplash

A caress lies down on the rough crests of the ocean … A caress lies down on my face which interposes its veil

Una carezza si corica sulle creste agitate dell’oceano … Una carezza si corica sul mio viso che interpone il suo velo

– from the song ‘Fingendo la Poesia’ (2003) by Marlene Kuntz

L et’s fast-forward to about 2,300 years ago. The foundation of many overseas settlements has proven Odysseus’ seaborne outlook true. But seldom do the affairs of city-states advance peaceably. Communities are torn by factions and civil strife. Athens and Sparta twice defeat an alliance of Persians and other Greeks. Athens destroys the Persian fleet at Salamis, rules the waves for some 70 years, and loses its own fleet at Syracuse. Sparta defeats Athens. Thebes defeats Sparta. The Macedonians defeat them all. Alexander the Great gets even with the Persians, pushing his army eastward to unimaginable distances. In Macedon-ruled Athens, Alexander’s tutor, Aristotle, opens the ‘Lyceum institute for advanced study’, as Paul Cartledge puts it in Ancient Greece (2011). Its members, dubbed Peripatetics, systematically review and reshape the knowledge of humankind and the outside world. When Alexander passes away too soon, Aristotle promptly flees an unsafe city. The mantle of Lyceum director passes to the deputy Theophrastus for a 35-year tenure across the period 320-280 BCE.

Meanwhile, the Odyssey has become an evergreen saga recited in an elevated archaic dialect. The corpus of poetry and prose has since bloomed in size and sophistication. Aided by such a versatile language, works of lyric, comedy, tragedy, oratory, historiography and philosophy grow countless. Alas, the ensuing 2,300 years take a heavy toll on humans and documents alike, and that vast trove is largely lost to us. While history granted preservation to the Odyssey , such favours were denied to the Lyceum library. The whirlpool of oblivion sank a good four-fifths of Aristotle’s works and most of Theophrastus’. Their works were badly preserved or lost early on. Moreover, antiquity did not value copyrights. The greater the authors’ standing, such as Aristotle’s and Theophrastus’, the stronger the appeal of editing their work to allure new readers. Lastly, copyists duplicated manuscripts by hand imperfectly. So, annotations, misreads, omissions and oversights could have altered much in the originals. And the writing in the Lyceum discussing sea waves stands out for its difficulty. It is called the Problems , an ominous-enough title for a work with puzzling fragments aplenty.

The Problems gathered many miscellaneous topics discussed at the Lyceum, at some point reworked into questions and answers, and grouped not so orderly within thematic chapters. Its table of contents affords suggestions for lottery players, like the disquisitions on waves being numbers 1, 2, 11, 12, 17, 24 and 28 inside Book 23, the chapter on marine matters. It counts as a blessing if a question has an answer, an answer a complete explanation, and an explanation clear reasoning. The original sources could have been accomplished treatises as well as drills for philosophical debates. The attribution of single disquisitions to named Peripatetics is challenging. More in the sense of a potpourri of cut-and-pastes than of the tightly woven fabric of the Odyssey , the Problems too could be regarded as a collective work.

They patched holes and matched edges resorting to first principles abstracted out of experience

A sure thing is that stories of waves were discussed time and time again. Their coming and going, their growing and settling, are dominant and primordial evidence. For all the lost libraries, tacit knowledge of waves was ocean-sized for a sailing folk whose livelihood and safety depended on the sea. The life of waves could also be contemplated mundanely: they could blow waves over a cupful of a hot drink or, sobriety ebbing in a torch-lit party, of wine that sparkled like the sea.

Within a philosophical school attentive to the natural world, then, the sea had to prompt its fair share of questions. A genuine Lyceum research motive could have been: given that something is seen to happen, owing to what does it happen? The Peripatetics gathered knowledge worth believing through empirical investigations. For matters out of their immediate grasp, they patched holes and matched edges resorting to first principles abstracted out of experience. Trademark tenets of the early Lyceum were, for example, the distinction between efficient, final, formal and material causes; natural changes as continual generation and perishing; air, earth, fire and water as the constituents of matter; hot, cold, moist and dry as its qualities. What about the cause of waves?

Granted that the wind generates waves, Odysseus confronted them long after the storm had ceased. Swell approaches the shore from far off on a windless day too. So, what pushes the waves ahead of the wind? What explains their rolling without it? This was a juicy study case for the Peripatetics keen on causes and effects. In a disquisition in the Problems , wind and waves are first acknowledged not to stop simultaneously ( οὐχ ἅμα παύεται τὸ πνεῦμα πνέον καὶ ἡ θάλαττα κυμαίνουσα ). Their explanation of swell propagation was that the wind’s initial action takes a seaborne route because nearby waters can set one another in motion faster than the air blows ( ἅμα δὲ πνεῖ καὶ τὴν πλησίον θάλατταν κινεῖ, αὕτη δὲ τὴν ἐχομένην … ὑπὸ γὰρ τη̃ς θαλάσσης καὶ οὐχ ὑπὸ του̃ πνεύματος ἡ κίνησις ἡ θάττων του̃ ἀέρος ἡ τη̃ς θαλάττης ). That novel causal chain may seem obvious to us. It lacks hints at the looping motion of particles in the top seawater, for example. But that’s surely one step beyond lamenting Poseidon’s whims.

Recognising that the wavy motion vanishes deeper down would have been a substantial challenge in those days. At depths where humans can dive unaided, the ancient Greeks would probably have observed only waves whose motion touches the seabed, for example while swaying sea grass. Also, a swimmer avoids the brunt of waves on the verge of breaking by diving below them. Experiencing the crest’s weight sweeping above one’s body would have been as compelling a validation as walking into stiff wind on land. Plausibly, their baseline belief was that the waviness at sea was some kind of advancing push, persisting down to the seabed, inshore and by extension offshore. And we cannot either hastily exclude that they had not thought of anything else; how not to exclude, actually, that they would have chuckled at the poor mangled texts arrived to us? Respect of textual evidence compels us to not harshly contrive allegations of conceptual shortcomings.

After being generated by wind, offshore waves will not move the water all the way down to the seafloor, but many transformations occur as they roll over shallow shores. The decreasing amount of water underneath them affects the flow motion under the surface, their shape, their advance speed and how they progressively move on. We have touched upon refraction with paraplegâs . For another reason, the valleys between pairs of waves accentuate as the aspect of crests and troughs becomes steeper. As waves get closer to the shore, the crests move faster than the troughs. Eventually, the water atop plunges off the wave base or spills down its face, depending on how steeply the seabed slopes. The onshore breakers are the final act after a journey across the sea’s expanse. The Peripatetics, keen on processes of generation and perishing, had prompts aplenty to pursue an enquiry on the full life cycle of waves. In their descriptions, however, both shore and laughter play an expected and unexpected role respectively.

The water in motion down to the seafloor was certainly too much for air to bring about that wonder of disintegration

Another disquisition in the Problems wonders why waves laugh only in narrow and shallow waters ( Διὰ τί τὸ κῦμα οὐκ ἐπιγελᾷ ἐν τοῖς βαθέσι πελάγεσιν ἀλλ’ ἐν τοῖς μικροῖς καὶ βραχέσιν ). A perplexity arrests our interpretation, as it did with paraplêgas in Odysseus’ landfall. At face value – pun intended – bouts of laughter may sound odd here. Conveniently, the disquisition itself identifies breakers onshore soon enough, since the laughing wave gets shattered loudly ( διὸ θραύεται πατάξαν μᾶλλον ). What made words of laughter suited to expressing wave-breaking, then? The onset of laughter can be felt as a wavelike build-up and release of tension. Both laughter and waves give us something to see and hear simultaneously. Loud laughter entails a sudden discomposure similar to the crash and splash of seawater, a snap moment of energy unleashed, which produces brightness and loud noise, as do faces engaged in bouts of laughter. Uniquely, that parallel stands up to the abrupt changes that each undergoes naturally: contrast glittering and plashing ripples with chuckles, and glaring and roaring bellows with guffaws.

Surprisingly, the physics-inspired analogy between breakers and laughter opens up one more route into the meaning of ancient Greek texts. Without recognition of the underwater side of waves, the clever operator behind onshore breaking among the four Peripatetic elements was air, as distinct from the wind that stirs waves. Shallow shores conditioned the readiness of waves to break since only there could air cause the total shattering into spray and foam of small amounts of moving water ( τὸ μικρὸν φερόμενον ὕδωρ διαιρεῖται ὑπὸ τοῦ ἀέρος μᾶλλον ἢ τὸ πολύ … ἐν μὲν οὖν τῷ βαθεῖ πολὺ τὸ κινούμενον, ἐν δὲ τῷ βραχεῖ ὀλίγον ). Farther offshore, the water in motion down to the seafloor was certainly too much for air to bring about that wonder of disintegration – an intriguing take upon bursts of human laughter, incidentally.

The analogy between sea states and laughter was not Lyceum coinage. In ancient Greek, the terms for simple laughter ( gélos , γέλως ) and sea calm ( galéne , γαλήνη ) originated from the same word family – the g and l clue up towards that kinship. For them, associating the absence or mildness of waves with laughing seascapes rang like a recognisable repetition. It evoked a serenity similar to what we might call our ‘smiling landscapes’. After all, we too project an emotion of calmness into our word choice when we say the sea is calm. The linguistic unity behind that analogy was already in the bud in Indo-European, the reconstructed ancestor of ancient Greek, around the 4th millennium BCE, well before the existence of any library that we lost.

Drawing on the natural versatility of human laughter and on the nuances of words for it, in small leaps of imagination the ancient Greek speaker could vividly articulate manifestations of the sea. Pointing at fits of laughter while waves broke could have sparked sympathetic laughter and reinforced that association in mirth. Compound words again conveyed that extension of meaning: the Peripatetics’ epigelân ( ἐπιγελα̃ν ), literally ‘to laugh up’, recalled social laughter that punctuates speech. The evolving language kept on enhancing an ancestral association with features of detail: the calm sea laughs calmly, and breakers laugh louder at its fringes.

Neither was the analogy between breakers and laughter Lyceum coinage. Aristotle’s and Theophrastus’ master, Plato, was into that linguistic association, for example. In his long dialogue on politics, the Republic , he compares a weighty philosophical discussion to falling into water and swimming on until one is rescued from all that reasoning – a veiled return to Odysseus’ longing for a faraway shore ( ἄντε τις εἰς κολυμβήθραν μικρὰν ἐμπέσῃ ἄντε εἰς τὸ μέγιστον πέλαγος μέσον, ὅμως γε νεῖ οὐδὲν ἡ̃ττον … Οὐκοῦν καὶ ἡμῖν νευστέον καὶ πειρατέον σῴζεσθαι ἐκ τοῦ λόγου ). Communal upbringing, parity of women and the philosophers’ rule are the three mainstays of the Republic that command careful handling. Bringing forth untenable arguments for them is the gravest failure that their proponent, Socrates, wants to avoid like the brunt of waves flushing him ashore ( Του̃το μὲν τοίνυν ἓν ὥσπερ κυ̃μα φω̃μεν διαφεύγειν … ὥστε μὴ παντάπασι κατακλυσθη̃ναι ).

W hile the size of those metaphorical waves ranked with the growing stakes of each topic, in a twist, the third and largest one threatens not just Socrates’ safety but also his reputation. And the task of discharging on him both commonplace discredit and peculiar laughter falls fastidiously on a laughing wave ( εἰρήσεται δ’ οὖν, εἰ καὶ μέλλει γέλωτί τε ἀτεχνῶς ὥσπερ κῦμα ἐκγελῶν καὶ ἀδοξίᾳ κατακλύσειν ). Wave-laughing expressing wave-breaking cued a fine piece of wordplay here, for the breaker released a flood of laughter precisely because it just had to laugh as it broke. Both the transformation and the dispatch of that Greek wave are Greek laughter. A master of verbal versatility, Plato preferred the compound verb ekgelân ( ἐκγελα̃ν ), literally ‘to laugh out’, a nuance for spontaneous and uncontainable laughter, as when one is tickled. As appropriate to political discussions, his irreverent billow laughed more raucously than the natural philosophers’ .

The more, and the more diverse, the attestations of laughter for waves survived in the records, the sounder that association belonged to everyday language. This is indeed the analysis in the essay ‘Laughing Waves in Ancient Greece’ (2023) in Classical Philology, which I co-authored with Francesco Giuseppe Sirna. In earlier centuries, Aeschylus the tragedian tells of the expanse’s laughter carrying on perpetually to the face of Prometheus in chains. In later centuries, Strabo the encyclopaedist tells of laughing waters off a delta hampering anchorage by breaking like the waves that the lifesaving river held up for Odysseus. Apparently, the ancient Greek waves could laugh into breakers as much as we can break into laughter. With such a line-up of attestations, it is tempting to imagine that, in the shore, the ancient Greeks saw the sea’s mouth where a fringe of breaking waves laughed loudly. After all, we speak of mouths of rivers, as did they. Why shouldn’t the sea have one too?

You may have heard of sea-level rise, tipping points and changes that unfold faster than previously reckoned upon. Regardless, waves will be the makers and breakers – pun intended – of our coasts. Whatever the fate of weather and climate as we know them, on the shore the sea will keep on having its everlasting laugh.

To the memory of the beaches already eroded from where my earliest childhood memories of the sea were impressed. To the memory of F G Sirna (1935-2023), over time a teacher, mentor, friend and collaborator, each of these quite a different cup of tea.

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