Download or read book Machine Learning for Speaker Recognition written by Man-Wai Mak and published by Cambridge University Press. This book was released on 2020-11-19 with total page 329 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book will help readers understand fundamental and advanced statistical models and deep learning models for robust speaker recognition and domain adaptation. This useful toolkit enables readers to apply machine learning techniques to address practical issues, such as robustness under adverse acoustic environments and domain mismatch, when deploying speaker recognition systems. Presenting state-of-the-art machine learning techniques for speaker recognition and featuring a range of probabilistic models, learning algorithms, case studies, and new trends and directions for speaker recognition based on modern machine learning and deep learning, this is the perfect resource for graduates, researchers, practitioners and engineers in electrical engineering, computer science and applied mathematics.
Download or read book Speaker Recognition written by Fouad Sabry and published by One Billion Knowledgeable. This book was released on 2023-07-06 with total page 157 pages. Available in PDF, EPUB and Kindle. Book excerpt: What Is Speaker Recognition The identification of a person based on the features of their voice is referred to as "speaker recognition." The purpose of this information is to provide an answer to the query "Who is speaking?" Speech recognition and speaker recognition are both included in the broader concept of voice recognition. Verification of a speaker is distinct from identification of a speaker, and recognition of a speaker is not the same as diarization of a speaker. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Speaker recognition Chapter 2: Speech recognition Chapter 3: Voice analysis Chapter 4: Authentication Chapter 5: Interactive voice response Chapter 6: Biometrics Chapter 7: Electronic authentication Chapter 8: Multi-factor authentication Chapter 9: BioAPI Chapter 10: PerSay (II) Answering the public top questions about speaker recognition. (III) Real world examples for the usage of speaker recognition in many fields. (IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of speaker recognition' technologies. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of speaker recognition.
Download or read book Deep Learning for NLP and Speech Recognition written by Uday Kamath and published by Springer. This book was released on 2019-06-10 with total page 621 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook explains Deep Learning Architecture, with applications to various NLP Tasks, including Document Classification, Machine Translation, Language Modeling, and Speech Recognition. With the widespread adoption of deep learning, natural language processing (NLP),and speech applications in many areas (including Finance, Healthcare, and Government) there is a growing need for one comprehensive resource that maps deep learning techniques to NLP and speech and provides insights into using the tools and libraries for real-world applications. Deep Learning for NLP and Speech Recognition explains recent deep learning methods applicable to NLP and speech, provides state-of-the-art approaches, and offers real-world case studies with code to provide hands-on experience. Many books focus on deep learning theory or deep learning for NLP-specific tasks while others are cookbooks for tools and libraries, but the constant flux of new algorithms, tools, frameworks, and libraries in a rapidly evolving landscape means that there are few available texts that offer the material in this book. The book is organized into three parts, aligning to different groups of readers and their expertise. The three parts are: Machine Learning, NLP, and Speech Introduction The first part has three chapters that introduce readers to the fields of NLP, speech recognition, deep learning and machine learning with basic theory and hands-on case studies using Python-based tools and libraries. Deep Learning Basics The five chapters in the second part introduce deep learning and various topics that are crucial for speech and text processing, including word embeddings, convolutional neural networks, recurrent neural networks and speech recognition basics. Theory, practical tips, state-of-the-art methods, experimentations and analysis in using the methods discussed in theory on real-world tasks. Advanced Deep Learning Techniques for Text and Speech The third part has five chapters that discuss the latest and cutting-edge research in the areas of deep learning that intersect with NLP and speech. Topics including attention mechanisms, memory augmented networks, transfer learning, multi-task learning, domain adaptation, reinforcement learning, and end-to-end deep learning for speech recognition are covered using case studies.
Download or read book Machine Learning for Speaker Recognition written by Man-Wai Mak and published by Cambridge University Press. This book was released on 2020-11-19 with total page 329 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn fundamental and advanced machine learning techniques for robust speaker recognition and domain adaptation with this useful toolkit.
Download or read book Fundamentals of Speaker Recognition written by Homayoon Beigi and published by Springer Science & Business Media. This book was released on 2011-12-09 with total page 984 pages. Available in PDF, EPUB and Kindle. Book excerpt: An emerging technology, Speaker Recognition is becoming well-known for providing voice authentication over the telephone for helpdesks, call centres and other enterprise businesses for business process automation. "Fundamentals of Speaker Recognition" introduces Speaker Identification, Speaker Verification, Speaker (Audio Event) Classification, Speaker Detection, Speaker Tracking and more. The technical problems are rigorously defined, and a complete picture is made of the relevance of the discussed algorithms and their usage in building a comprehensive Speaker Recognition System. Designed as a textbook with examples and exercises at the end of each chapter, "Fundamentals of Speaker Recognition" is suitable for advanced-level students in computer science and engineering, concentrating on biometrics, speech recognition, pattern recognition, signal processing and, specifically, speaker recognition. It is also a valuable reference for developers of commercial technology and for speech scientists. Please click on the link under "Additional Information" to view supplemental information including the Table of Contents and Index.
Download or read book Automatic Speech and Speaker Recognition written by Joseph Keshet and published by John Wiley & Sons. This book was released on 2009-04-27 with total page 268 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book discusses large margin and kernel methods for speech and speaker recognition Speech and Speaker Recognition: Large Margin and Kernel Methods is a collation of research in the recent advances in large margin and kernel methods, as applied to the field of speech and speaker recognition. It presents theoretical and practical foundations of these methods, from support vector machines to large margin methods for structured learning. It also provides examples of large margin based acoustic modelling for continuous speech recognizers, where the grounds for practical large margin sequence learning are set. Large margin methods for discriminative language modelling and text independent speaker verification are also addressed in this book. Key Features: Provides an up-to-date snapshot of the current state of research in this field Covers important aspects of extending the binary support vector machine to speech and speaker recognition applications Discusses large margin and kernel method algorithms for sequence prediction required for acoustic modeling Reviews past and present work on discriminative training of language models, and describes different large margin algorithms for the application of part-of-speech tagging Surveys recent work on the use of kernel approaches to text-independent speaker verification, and introduces the main concepts and algorithms Surveys recent work on kernel approaches to learning a similarity matrix from data This book will be of interest to researchers, practitioners, engineers, and scientists in speech processing and machine learning fields.
Download or read book Speech Recognition using Deep Learning written by Dr. Narendrababu Reddy G, and published by Archers & Elevators Publishing House. This book was released on with total page 50 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Speaker Classification II written by C. Müller and published by Springer Science & Business Media. This book was released on 2007-08-15 with total page 317 pages. Available in PDF, EPUB and Kindle. Book excerpt: This two-volume set constitutes a state-of-the-art survey in the field of speaker classification, addressing many critical questions. The twenty-two articles of the second volume cover a number of areas, including gender recognition systems, emotion recognition, text-dependent speaker verification systems, an analysis of both speaker and verbal content information, and accent identification.
Download or read book Speech Recognition written by Fouad Sabry and published by One Billion Knowledgeable. This book was released on 2022-07-10 with total page 435 pages. Available in PDF, EPUB and Kindle. Book excerpt: What Is Speech Recognition Computer science and computational linguistics have spawned a subfield known as speech recognition, which is an interdisciplinary field that focuses on the development of methodologies and technologies that enable computers to recognize and translate spoken language into text. The primary advantage of this is that the text can then be searched. Automatic speech recognition, sometimes abbreviated as ASR, is another name for it, as is computer speech recognition and voice to text (STT). The domains of computer science, linguistics, and computer engineering are all represented in its incorporation of knowledge and study. Speech synthesis is the process of doing things backwards. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Speech recognition Chapter 2: Computational linguistics Chapter 3: Natural language processing Chapter 4: Speech processing Chapter 5: Speech synthesis Chapter 6: Vector quantization Chapter 7: Pattern recognition Chapter 8: Lawrence Rabiner Chapter 9: Recurrent neural network Chapter 10: Julius (software) Chapter 11: Long short-term memory Chapter 12: Time delay neural network Chapter 13: Types of artificial neural networks Chapter 14: Deep learning Chapter 15: Nelson Morgan Chapter 16: Sinsy Chapter 17: Outline of machine learning Chapter 18: Steve Young (academic) Chapter 19: Tony Robinson (speech recognition) Chapter 20: Voice computing Chapter 21: Joseph Keshet (II) Answering the public top questions about speech recognition. (III) Real world examples for the usage of speech recognition in many fields. (IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of speech recognition' technologies. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of speech recognition.
Download or read book Text Dependent Speaker Recognition A Machine Learning Approach written by Shruti Gujral and published by LAP Lambert Academic Publishing. This book was released on 2013 with total page 72 pages. Available in PDF, EPUB and Kindle. Book excerpt: Most of us are aware of the fact that voices of different individuals do not sound alike. This important property of speech being speaker dependent is what enables us to recognize a friend over a telephone. The ability of recognizing a person solely from his voice is known as speaker recognition. Applications of speaker recognition are becoming ubiquitous, especially after the increased terrorist activities around the world. Speech patterns can prove extremely useful in criminal investigation and litigation. Moreover speaker recognition can be useful to adapt the machines into their users because a speech interface, in a user's own language is ideal as it is the most natural, flexible, efficient, and economical form of human communication. Speaker recognition can be a useful tool in many other areas such as forensic speech analysis. The choice of features plays an important role in the performance of recognizer. Here we propose prosodic feature set for text dependent speaker recognition. Further we propose the use of Machine Learning (ML) algorithms as classifier to overcome the drawbacks of traditional classifiers.
Download or read book Deep Learning for Speech Classification and Speaker Recognition written by Muhammad Muneeb Saleem and published by . This book was released on 2014 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning is the state-of-the-art technique in machine learning with applications in speech recognition. In this study, an efficient system is formulated to process large amounts of speech data within the deep learning framework by harnessing the parallel processing power of High-Performance Computing oriented Graphics Processing Unit (GPU). This thesis focuses on applications of this approach to address stressed speech classification as well as discrimination between different flavors of noise-free speech under Lombard Effect. Different architectures of deep neural networks (DNN) are explored to build state-of-the-art classifiers for detection and classification of stressed speech and Lombard Effect flavors. Furthermore, applications of deep networks are explored to improve current state-of-the-art speaker recognition systems. Further integration of discriminative deep architectures is accomplished for unsupervised methods in training front-ends for Speaker Recognition Evaluation systems.
Download or read book Speaker Classification I written by Christian Müller and published by Springer. This book was released on 2007-08-28 with total page 358 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume and its companion volume LNAI 4441 constitute a state-of-the-art survey in the field of speaker classification. Together they address such intriguing issues as how speaker characteristics are manifested in voice and speaking behavior. The nineteen contributions in this volume are organized into topical sections covering fundamentals, characteristics, applications, methods, and evaluation.
Download or read book Automatic Speech Recognition written by Dong Yu and published by Springer. This book was released on 2014-11-11 with total page 329 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a comprehensive overview of the recent advancement in the field of automatic speech recognition with a focus on deep learning models including deep neural networks and many of their variants. This is the first automatic speech recognition book dedicated to the deep learning approach. In addition to the rigorous mathematical treatment of the subject, the book also presents insights and theoretical foundation of a series of highly successful deep learning models.
Download or read book Robust Speaker Recognition in Noisy Environments written by K. Sreenivasa Rao and published by Springer. This book was released on 2014-06-21 with total page 149 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book discusses speaker recognition methods to deal with realistic variable noisy environments. The text covers authentication systems for; robust noisy background environments, functions in real time and incorporated in mobile devices. The book focuses on different approaches to enhance the accuracy of speaker recognition in presence of varying background environments. The authors examine: (a) Feature compensation using multiple background models, (b) Feature mapping using data-driven stochastic models, (c) Design of super vector- based GMM-SVM framework for robust speaker recognition, (d) Total variability modeling (i-vectors) in a discriminative framework and (e) Boosting method to fuse evidences from multiple SVM models.
Download or read book Comparison of Machine Learning Methods for a Speaker Recognition System written by University of Guelph. School of Engineering and published by . This book was released on 2009 with total page 80 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book New Era for Robust Speech Recognition written by Shinji Watanabe and published by Springer. This book was released on 2017-10-30 with total page 436 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers the state-of-the-art in deep neural-network-based methods for noise robustness in distant speech recognition applications. It provides insights and detailed descriptions of some of the new concepts and key technologies in the field, including novel architectures for speech enhancement, microphone arrays, robust features, acoustic model adaptation, training data augmentation, and training criteria. The contributed chapters also include descriptions of real-world applications, benchmark tools and datasets widely used in the field. This book is intended for researchers and practitioners working in the field of speech processing and recognition who are interested in the latest deep learning techniques for noise robustness. It will also be of interest to graduate students in electrical engineering or computer science, who will find it a useful guide to this field of research.
Download or read book Concepts and Real Time Applications of Deep Learning written by Smriti Srivastava and published by Springer Nature. This book was released on 2021-09-23 with total page 212 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides readers with a comprehensive and recent exposition in deep learning and its multidisciplinary applications, with a concentration on advances of deep learning architectures. The book discusses various artificial intelligence (AI) techniques based on deep learning architecture with applications in natural language processing, semantic knowledge, forecasting and many more. The authors shed light on various applications that can benefit from the use of deep learning in pattern recognition, person re-identification in surveillance videos, action recognition in videos, image and video captioning. The book also highlights how deep learning concepts can be interwoven with more modern concepts to yield applications in multidisciplinary fields. Presents a comprehensive look at deep learning and its multidisciplinary applications, concentrating on advances of deep learning architectures; Includes a survey of deep learning problems and solutions, identifying the main open issues, innovations and latest technologies; Shows industrial deep learning in practice with examples/cases, efforts, challenges, and strategic approaches.
