speech recognition book pdf

7 books on Speech Recognition [PDF]

Audio Processing and Speech Recognition: Concepts, Techniques and Research Overviews

• December 2018
• SpringerBriefs in Applied Sciences and Technology
• Publisher: SpringerBriefs in Applied Sciences and Technology
• ISBN: ISBN-10: 9811360979

• University of Calcutta

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Speech and language processing : an introduction to natural language processing, computational linguistics, and speech recognition

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Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition

Related Papers

Trinadh Veeramachaneni

Communications of the ACM

Inderjeet Mani

Mary Joy Galagar

Linguistics is the study and the description of human languages. Linguistic theories on grammar and meaning have been developed since ancient times and the Middle Ages. However, modern linguistics originated at the end of the nineteenth century and the beginning of the twentieth century. Its founder and most prominent figure was probably Ferdinand de Saussure (1916). Over time, modern linguistics has produced an impressive set of descriptions and theories. Computational linguistics is a subset of both linguistics and computer science. Its goal is to design mathematical models of language structures enabling the automation of language processing by a computer. From a linguist's viewpoint, we can consider computational linguistics as the formalization of linguistic theories and models or their implementation in a machine. We can also view it as a means to develop new linguistic theories with the aid of a computer. From an applied and industrial viewpoint, language and speech processing, which is sometimes referred to as natural language processing (NLP) or natural language understanding (NLU), is the mechanization of human language faculties. People use language every day in conversations by listening and talking, or by reading and writing. It is probably our preferred mode of communication and interaction. Ideally, automated language processing would enable a computer to understand texts or speech and to interact accordingly with human beings. Understanding or translating texts automatically and talking to an artificial conversational assistant are major challenges for the computer industry. Although this final goal has not been reached yet, in spite of constant research, it is being approached every day, step-by-step. Even if we have missed Stanley Kubrick's prediction of talking electronic creatures in the year 2001, language processing and understanding techniques have already achieved results ranging from very promising to near perfect. The description of these techniques is the subject of this book.

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Computational Linguistics, as a subfield of Linguistics, or Natural Language Processing (NLP), as a subfield of Artificial Intelligence (two research areas that nowadays can be safely considered as merged) concentrate on the “study of computer systems for understanding and generating natural language”[10], in order to develop “a computational theory of language, using the notions of algorithms and data structures from Computer Science”[2].

Ali Farghaly

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Speech Recognition

March 24, 2006

Chapters in the first part of the book cover all the essential speech processing techniques for building robust, automatic speech recognition systems: the representation for speech signals and the methods for speech-features extraction, acoustic and language modeling, efficient algorithms for searching the hypothesis space, and multimodal approaches to speech recognition.

Book Description

The last part of the book is devoted to other speech processing applications that can use the information from automatic speech recognition for speaker identification and tracking, for prosody modeling in emotion-detection systems and in other speech processing applications that are able to operate in real-world environments, like mobile communication services and smart homes.

Table of Contents

• A Family of Stereo-Based Stochastic Mapping Algorithms for Noisy Speech Recognition
• Histogram Equalization for Robust Speech Recognition
• Employment of Spectral Voicing Information for Speech and Speaker Recognition in Noisy Conditions
• Time-Frequency Masking: Linking Blind Source Separation and Robust Speech Recognition
• Dereverberation and Denoising Techniques for ASR Applications
• Feature Transformation Based on Generalization of Linear Discriminant Analysis
• Algorithms for Joint Evaluation of Multiple Speech Patterns for Automatic Speech Recognition
• Overcoming HMM Time and Parameter Independence Assumptions for ASR
• Practical Issues of Building Robust HMM Models Using HTK and SPHINX Systems
• Statistical Language Modeling for Automatic Speech Recognition of Agglutinative Languages
• Discovery of Words: towards a Computational Model of Language Acquisition
• Automatic Speech Recognition via N-Best Rescoring using Logistic Regression
• Knowledge Resources in Automatic Speech Recognition and Understanding for Romanian Language
• Construction of a Noise-Robust Body-Conducted Speech Recognition System
• Adaptive Decision Fusion for Audio-Visual Speech Recognition
• Multi-Stream Asynchrony Modeling for Audio Visual Speech Recognition
• Normalization and Transformation Techniques for Robust Speaker Recognition
• Speaker Vector-Based Speaker Recognition with Phonetic Modeling
• Novel Approaches to Speaker Clustering for Speaker Diarization in Audio Broadcast News Data
• Gender Classification in Emotional Speech
• Recognition of Paralinguistic Information using Prosodic Features Related to Intonation and Voice Quality
• Psychological Motivated Multi-Stage Emotion Classification Exploiting Voice Quality Features
• A Weighted Discrete KNN Method for Mandarin Speech and Emotion Recognition
• Motion-Tracking and Speech Recognition for Hands-Free Mouse-Pointer Manipulation
• Arabic Dialectical Speech Recognition in Mobile Communication Services
• Ultimate Trends in Integrated Systems to Enhance Automatic Speech Recognition Performance
• Speech Recognition for Smart Homes
• Silicon Technologies for Speaker Independent Speech Processing and Recognition Systems in Noisy Environments
• Voice Activated Appliances for Severely Disabled Persons
• System Request Utterance Detection Based on Acoustic and Linguistic Features

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• Assignments

CS224S: Spoken Language Processing

Spring 2024.

Introduction to spoken language technology with an emphasis on dialog and conversational systems. Deep learning and other methods for automatic speech recognition, speech synthesis, affect detection, dialogue management, and applications to digital assistants and spoken language understanding systems.

Time and Location

Mon. & Wed. 12:30 PM - 1:20 PM Pacific Time Jordan Hall room 040 (420-040)

Poster Session

Please join us in person for the final projet poster session!

Anyone with Stanford affiliation, and members of the spoken language research/industry community are welcome to join us Wednesday June 5 for a final project poster session. In Spoken Language Processing this year we have about 65 student groups with project topics ranging from speech synthesis with style transfer to exploring foundation model features for spoken language tasks, and even building speech datasets for new languages! Each group will present a poster and be available for questions/discussion as guests circulate.

When: Wednesday June 5, 2024 . 12:30pm - 2:00pm

Where: Mackenzie Room. Jen-Hsun Huang Engineering Center . Stanford Campus

What: Spoken Language Processing Class Project Poster Session

Who: We welcome members of the Stanford and Speech/NLP communities

Course Information

This course is designed around lectures, assignments, and a course project to give students practical experience building spoken language systems. We will use modern software tools and algorithmic approaches. There are no exams. We aim for each student to build something they are proud of.

There are three homeworks. Homework topics:

• Introduction to audio analysis and speech synthesis tools
• Working with speech recognition toolkits and APIs
• Leveraging audio foundation models and working with non-English speech tasks

Course projects can range from algorithmic research with the goal of publishing academic papers, or designing and demonstrating spoken language systems.

Lectures are Mondays and Wednesdays, 12:30pm - 1:20pm Pacific time. The lecture venue is Jordan Hall room 040 ( 420-040 ), which is on the lower level of Jordan Hall and accessible via outside doors from the lower courtyard behind Jordan Hall. Lectures will be held in person and students are strongly encouraged to participate in person. We will record lectures using Zoom and make recordings available on Canvas after class (only available to enrolled students).

Please use Ed Discussion for all communication related to the course. We encourage you to keep posts public when possible in order to prevent duplication. For private matters, please either make a private post visible only to the course instructors or email [email protected] . For longer discussions, we strongly encourage you to use office hours.

Course Staff

Course Assistants

Office hours.

Andrew Maas : Monday & Wednesday 1:20 - 2:00 PM | In person. Outside of lecture hall after class. Gautham Raghupathi : Monday 3:15 pm - 4:15 pm. Zoom link (password: 577468) Fahad Nabi : Tuesday 5:45 pm - 7 pm. Zoom link Abhinav Garg : Wednesday 9 am - 10 am. Zoom link Tolúlọpẹ́ Ogunremi : Thursday 10:30 am - 11:30 am. Zoom link

• Homework 1: 11%
• Homework 2: 12%
• Homework 3: 12%
• Course Project: 60%. Point breakdown for project will be provided as part of the course project handout. Final report and poster are the main components of course project grade.
• Attending each of the 6 guest lectures in the course, or ask a question in advance on Ed if you are unable to attend. 0.5% each lecture
• Ed contributions. We will award 2% to the top 10 Ed contributors. All other students will receive a fraction of 2% based on their contributions relative to the 10th highest contributor. (e.g. 0.5 * 2% for 50% contribution level compared with 10th highest student)

All assignments are to be submitted via our Gradescope. Each student will have a total of five (5) free late (calendar) days to use for homeworks. Once these late days are exhausted, any assignments turned in late will be penalized 20% per late day. However, no assignment will be accepted more than three (3) days after its due date. Each 24 hours or part thereof that a homework is late uses up one full late day. Please note that late days are applied individually. Submitting a project deliverable late costs each group member one late day per day.

Regrades will also be handled through Gradescope. We will begin to accept regrade requests for an assignment the day after grades are released for a window of three days. We will not accept regrades for an assignment outside of that window. Regrades are intended to remedy grading errors, so regrade requests must discuss why you believe your answer is correct in light of the deduction you received. When you submit a regrade request, the grader may review your entire assignment, in which case you may lose points on other questions. Your score on an assignment may decrease if you submit for a regrade.

Prerequisites

Proficiency in Python. Homework assignments will be in a mixture of Python using PyTorch, Jupyter Notebooks, Amazon Skills Kit, and other tools. We attempt to make the course accessible to students with a basic programming background, but ideally students will have some experience with machine learning or natural language tasks in Python.

Foundations of Machine Learning and Natural Language Processing (CS 124, CS 129, CS 221, CS 224N, CS 229 or equivalent). You should be comfortable with basic concepts of machine learning and natural language processing. We do not strictly enforce a particular set of previous courses but students will have to fill in gaps on their own depending on background.

Useful Reference Texts

• Dan Jurafsky and James H. Martin. Speech and Language Processing (3rd ed. draft) [link]
• Yoav Goldberg. A Primer on Neural Network Models for Natural Language Processing [link]
• Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Deep Learning. MIT Press. [link]
• CS224N Python Tutorial [Notebook link] [Slides link]
• CS224N PyTorch Tutorial [link]

We encourage students to form study groups. Students may discuss and work on programming assignments and quizzes in groups. However, each student must write down the solutions independently, and without referring to written notes from the joint session. In other words, each student must understand the solution well enough in order to reconstruct it by him/herself. In addition, each student should submit his/her own code and mention anyone he/she collaborated with. It is also an honor code violation to copy, refer to, or look at written or code solutions from a previous year, including but not limited to: official solutions from a previous year, solutions posted online, and solutions you or someone else may have written up in a previous year. Furthermore, it is an honor code violation to post your assignment solutions online, such as on a public git repo.

AI Tools Policy

Students are required to independently submit their solutions for homework assignments. Collaboration with generative AI tools such as Co-Pilot and ChatGPT is allowed, treating them as collaborators in the problem-solving process. However, the direct solicitation of answers or copying solutions, whether from peers or external sources, is strictly prohibited. If you use tools to help complete the homework, please cite them in your report.

Employing AI tools to substantially complete assignments or the project is considered a violation of the Honor Code . For additional details, please refer to the Generative AI Policy Guidance here .

The Stanford Honor Code

The Stanford Honor Code as it pertains to CS courses

Speech Emotion Recognition: An Empirical Analysis of Machine Learning Algorithms Across Diverse Data Sets

• Conference paper
• First Online: 20 August 2024
• Cite this conference paper

• Mostafiz Ahammed   ORCID: orcid.org/0000-0003-2213-9241 8 ,
• Rubel Sheikh   ORCID: orcid.org/0000-0002-6824-340X 9 ,
• Farah Hossain 8 ,
• Shahrima Mustak Liza 8 ,
• Muhammad Arifur Rahman   ORCID: orcid.org/0000-0002-6774-0041 10 ,
• Mufti Mahmud   ORCID: orcid.org/0000-0002-2037-8348 10 , 11 &
• David J. Brown   ORCID: orcid.org/0000-0002-1677-7485 10  

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2065))

Included in the following conference series:

• International Conference on Applied Intelligence and Informatics

Communication is the way of expressing one’s feelings, ideas, and thoughts. Speech is a primary medium for communication. While people communicate with each other in several human interactive applications, such as a call center, entertainment, E-learning between teachers and students, medicine, and communication between clinicians and patients (especially important in the field of psychiatry), it is crucial to identify people’s emotions to better understand what they are feeling and how they might react in a range of situations. Automated systems are constructed to recognise emotions from analysis of speech or human voice using Artificial Intelligence (AI) or Machine Learning (ML) approaches, and these approaches are gaining momentum in recent research. This research aims to recognise a range of emotional states such as happy, sad, calm, angry, fear, disgust, surprise, or neutral from input speech signals with greater accuracy than currently seen in contemporary research. In order to achieve this aim, we have used the Support Vector Machine (SVM) classification algorithm and formed a feature vector by exploring speech features such as Mel Frequency Cepstral Coefficient (MFCC), Chroma, Mel-spectrogram, Spectral Centroid, Spectral Bandwidth, Spectral Roll-off, Root Mean Squared Energy (RMSE), and Zero Crossing Rate (ZCR) from speech signals. O. The system is tested on the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS), the Toronto Emotional Speech Set (TESS), and the Surrey Audio-Visual Expressed Emotion Database (SAVEE) datasets. Our proposed approach has achieved an overall accuracy of 99.59% on the RAVDESS dataset, 99.82% on the TESS dataset, and 98.95% on the SAVEE dataset for the SVM classifier. A mixed dataset is created from the three speech emotion datasets, which achieved significantly high classification accuracy compared with state-of-the-art methods. This model performs well on a large dataset, is ready to be tested with even bigger datasets, and can be used in a range of diverse applications, including education and clinical applications. GitHub: https://github.com/Mostafiz24/Speech-Emotion-Recognition .

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Savee dataset, 10 December 2020. http://kahlan.eps.surrey.ac.uk/savee/Download.html

Sjtu Chinese emotional dataset, 12 December 2020. https://bcmi.sjtu.edu.cn/home/seed/

Emo-db dataset, 15 December 2020. http://emodb.bilderbar.info/docu/

How to make a speech emotion recognizer using python, 26 December 2020. https://www.thepythoncode.com/article/building-a-speech-emotion-recognizer-using-sklearn

Ravdess dataset, 5 December 2020. http://zenodo.org/record/1188976

Tess dataset, 8 December 2020. https://doi.org/10.5683/SP2/E8H2MF

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Mostafiz Ahammed, Farah Hossain & Shahrima Mustak Liza

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Ahammed, M. et al. (2024). Speech Emotion Recognition: An Empirical Analysis of Machine Learning Algorithms Across Diverse Data Sets. In: Mahmud, M., Ben-Abdallah, H., Kaiser, M.S., Ahmed, M.R., Zhong, N. (eds) Applied Intelligence and Informatics. AII 2023. Communications in Computer and Information Science, vol 2065. Springer, Cham. https://doi.org/10.1007/978-3-031-68639-9_3

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