Discriminative Learning For Speech Recognition

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Discriminative Learning for Speech Recognition

Xiadong He,Xiaodong He,Li Deng
Discriminative Learning for Speech Recognition Book PDF

Author : Xiadong He,Xiaodong He,Li Deng
Publisher : Morgan & Claypool Publishers
Page : 121 pages
File Size : 49,5 Mb
Release : 2008
Category : Automatic speech recognition
ISBN : 9781598293081

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Discriminative Learning for Speech Recognition by Xiadong He,Xiaodong He,Li Deng Pdf

In this book, we introduce the background and mainstream methods of probabilistic modeling and discriminative parameter optimization for speech recognition. The specific models treated in depth include the widely used exponential-family distributions and the hidden Markov model. A detailed study is presented on unifying the common objective functions for discriminative learning in speech recognition, namely maximum mutual information (MMI), minimum classification error, and minimum phone/word error. The unification is presented, with rigorous mathematical analysis, in a common rational-function form. This common form enables the use of the growth transformation (or extended Baum-Welch) optimization framework in discriminative learning of model parameters. In addition to all the necessary introduction of the background and tutorial material on the subject, we also included technical details on the derivation of the parameter optimization formulas for exponential-family distributions, discrete hidden Markov models (HMMs), and continuous-density HMMs in discriminative learning. Selected experimental results obtained by the authors in firsthand are presented to show that discriminative learning can lead to superior speech recognition performance over conventional parameter learning. Details on major algorithmic implementation issues with practical significance are provided to enable the practitioners to directly reproduce the theory in the earlier part of the book into engineering practice.

Discriminative Learning for Speech Recognition

Xiadong He,Li Deng
Discriminative Learning for Speech Recognition Book PDF

Author : Xiadong He,Li Deng
Publisher : Springer Nature
Page : 112 pages
File Size : 52,9 Mb
Release : 2022-06-01
Category : Technology & Engineering
ISBN : 9783031025570

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Discriminative Learning for Speech Recognition by Xiadong He,Li Deng Pdf

In this book, we introduce the background and mainstream methods of probabilistic modeling and discriminative parameter optimization for speech recognition. The specific models treated in depth include the widely used exponential-family distributions and the hidden Markov model. A detailed study is presented on unifying the common objective functions for discriminative learning in speech recognition, namely maximum mutual information (MMI), minimum classification error, and minimum phone/word error. The unification is presented, with rigorous mathematical analysis, in a common rational-function form. This common form enables the use of the growth transformation (or extended Baum–Welch) optimization framework in discriminative learning of model parameters. In addition to all the necessary introduction of the background and tutorial material on the subject, we also included technical details on the derivation of the parameter optimization formulas for exponential-family distributions, discrete hidden Markov models (HMMs), and continuous-density HMMs in discriminative learning. Selected experimental results obtained by the authors in firsthand are presented to show that discriminative learning can lead to superior speech recognition performance over conventional parameter learning. Details on major algorithmic implementation issues with practical significance are provided to enable the practitioners to directly reproduce the theory in the earlier part of the book into engineering practice. Table of Contents: Introduction and Background / Statistical Speech Recognition: A Tutorial / Discriminative Learning: A Unified Objective Function / Discriminative Learning Algorithm for Exponential-Family Distributions / Discriminative Learning Algorithm for Hidden Markov Model / Practical Implementation of Discriminative Learning / Selected Experimental Results / Epilogue / Major Symbols Used in the Book and Their Descriptions / Mathematical Notation / Bibliography

Automatic Speech and Speaker Recognition

Joseph Keshet,Samy Bengio
Automatic Speech and Speaker Recognition Book PDF

Author : Joseph Keshet,Samy Bengio
Publisher : John Wiley & Sons
Page : 268 pages
File Size : 48,9 Mb
Release : 2009-04-27
Category : Technology & Engineering
ISBN : 0470742038

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Automatic Speech and Speaker Recognition by Joseph Keshet,Samy Bengio Pdf

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.

Automatic Speech Recognition

Dong Yu,Li Deng
Automatic Speech Recognition Book PDF

Author : Dong Yu,Li Deng
Publisher : Springer
Page : 329 pages
File Size : 51,6 Mb
Release : 2014-11-11
Category : Technology & Engineering
ISBN : 9781447157793

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Automatic Speech Recognition by Dong Yu,Li Deng Pdf

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.

New Era for Robust Speech Recognition

Shinji Watanabe,Marc Delcroix,Florian Metze,John R. Hershey
New Era for Robust Speech Recognition Book PDF

Author : Shinji Watanabe,Marc Delcroix,Florian Metze,John R. Hershey
Publisher : Springer
Page : 436 pages
File Size : 48,5 Mb
Release : 2017-10-30
Category : Computers
ISBN : 9783319646800

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New Era for Robust Speech Recognition by Shinji Watanabe,Marc Delcroix,Florian Metze,John R. Hershey Pdf

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.

Machine Learning in Signal Processing

Sudeep Tanwar,Anand Nayyar,Rudra Rameshwar
Machine Learning in Signal Processing Book PDF

Author : Sudeep Tanwar,Anand Nayyar,Rudra Rameshwar
Publisher : CRC Press
Page : 488 pages
File Size : 50,5 Mb
Release : 2021-12-10
Category : Technology & Engineering
ISBN : 9781000487817

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Machine Learning in Signal Processing by Sudeep Tanwar,Anand Nayyar,Rudra Rameshwar Pdf

Machine Learning in Signal Processing: Applications, Challenges, and the Road Ahead offers a comprehensive approach toward research orientation for familiarizing signal processing (SP) concepts to machine learning (ML). ML, as the driving force of the wave of artificial intelligence (AI), provides powerful solutions to many real-world technical and scientific challenges. This book will present the most recent and exciting advances in signal processing for ML. The focus is on understanding the contributions of signal processing and ML, and its aim to solve some of the biggest challenges in AI and ML. FEATURES Focuses on addressing the missing connection between signal processing and ML Provides a one-stop guide reference for readers Oriented toward material and flow with regards to general introduction and technical aspects Comprehensively elaborates on the material with examples and diagrams This book is a complete resource designed exclusively for advanced undergraduate students, post-graduate students, research scholars, faculties, and academicians of computer science and engineering, computer science and applications, and electronics and telecommunication engineering.

Robust Automatic Speech Recognition

Jinyu Li,Li Deng,Reinhold Haeb-Umbach,Yifan Gong
Robust Automatic Speech Recognition Book PDF

Author : Jinyu Li,Li Deng,Reinhold Haeb-Umbach,Yifan Gong
Publisher : Academic Press
Page : 306 pages
File Size : 47,7 Mb
Release : 2015-10-30
Category : Technology & Engineering
ISBN : 9780128026168

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Robust Automatic Speech Recognition by Jinyu Li,Li Deng,Reinhold Haeb-Umbach,Yifan Gong Pdf

Robust Automatic Speech Recognition: A Bridge to Practical Applications establishes a solid foundation for automatic speech recognition that is robust against acoustic environmental distortion. It provides a thorough overview of classical and modern noise-and reverberation robust techniques that have been developed over the past thirty years, with an emphasis on practical methods that have been proven to be successful and which are likely to be further developed for future applications. The strengths and weaknesses of robustness-enhancing speech recognition techniques are carefully analyzed. The book covers noise-robust techniques designed for acoustic models which are based on both Gaussian mixture models and deep neural networks. In addition, a guide to selecting the best methods for practical applications is provided. The reader will: Gain a unified, deep and systematic understanding of the state-of-the-art technologies for robust speech recognition Learn the links and relationship between alternative technologies for robust speech recognition Be able to use the technology analysis and categorization detailed in the book to guide future technology development Be able to develop new noise-robust methods in the current era of deep learning for acoustic modeling in speech recognition The first book that provides a comprehensive review on noise and reverberation robust speech recognition methods in the era of deep neural networks Connects robust speech recognition techniques to machine learning paradigms with rigorous mathematical treatment Provides elegant and structural ways to categorize and analyze noise-robust speech recognition techniques Written by leading researchers who have been actively working on the subject matter in both industrial and academic organizations for many years

Log-Linear Models, Extensions, and Applications

Aleksandr Aravkin,Anna Choromanska,Li Deng,Georg Heigold,Tony Jebara
Log Linear Models Extensions and Applications Book PDF

Author : Aleksandr Aravkin,Anna Choromanska,Li Deng,Georg Heigold,Tony Jebara
Publisher : MIT Press
Page : 215 pages
File Size : 41,8 Mb
Release : 2018-12-25
Category : Computers
ISBN : 9780262351614

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Log-Linear Models, Extensions, and Applications by Aleksandr Aravkin,Anna Choromanska,Li Deng,Georg Heigold,Tony Jebara Pdf

Advances in training models with log-linear structures, with topics including variable selection, the geometry of neural nets, and applications. Log-linear models play a key role in modern big data and machine learning applications. From simple binary classification models through partition functions, conditional random fields, and neural nets, log-linear structure is closely related to performance in certain applications and influences fitting techniques used to train models. This volume covers recent advances in training models with log-linear structures, covering the underlying geometry, optimization techniques, and multiple applications. The first chapter shows readers the inner workings of machine learning, providing insights into the geometry of log-linear and neural net models. The other chapters range from introductory material to optimization techniques to involved use cases. The book, which grew out of a NIPS workshop, is suitable for graduate students doing research in machine learning, in particular deep learning, variable selection, and applications to speech recognition. The contributors come from academia and industry, allowing readers to view the field from both perspectives. Contributors Aleksandr Aravkin, Avishy Carmi, Guillermo A. Cecchi, Anna Choromanska, Li Deng, Xinwei Deng, Jean Honorio, Tony Jebara, Huijing Jiang, Dimitri Kanevsky, Brian Kingsbury, Fabrice Lambert, Aurélie C. Lozano, Daniel Moskovich, Yuriy S. Polyakov, Bhuvana Ramabhadran, Irina Rish, Dimitris Samaras, Tara N. Sainath, Hagen Soltau, Serge F. Timashev, Ewout van den Berg

Techniques for Noise Robustness in Automatic Speech Recognition

Tuomas Virtanen,Rita Singh,Bhiksha Raj
Techniques for Noise Robustness in Automatic Speech Recognition Book PDF

Author : Tuomas Virtanen,Rita Singh,Bhiksha Raj
Publisher : John Wiley & Sons
Page : 514 pages
File Size : 41,6 Mb
Release : 2012-11-28
Category : Technology & Engineering
ISBN : 9781119970880

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Techniques for Noise Robustness in Automatic Speech Recognition by Tuomas Virtanen,Rita Singh,Bhiksha Raj Pdf

Automatic speech recognition (ASR) systems are finding increasing use in everyday life. Many of the commonplace environments where the systems are used are noisy, for example users calling up a voice search system from a busy cafeteria or a street. This can result in degraded speech recordings and adversely affect the performance of speech recognition systems. As the use of ASR systems increases, knowledge of the state-of-the-art in techniques to deal with such problems becomes critical to system and application engineers and researchers who work with or on ASR technologies. This book presents a comprehensive survey of the state-of-the-art in techniques used to improve the robustness of speech recognition systems to these degrading external influences. Key features: Reviews all the main noise robust ASR approaches, including signal separation, voice activity detection, robust feature extraction, model compensation and adaptation, missing data techniques and recognition of reverberant speech. Acts as a timely exposition of the topic in light of more widespread use in the future of ASR technology in challenging environments. Addresses robustness issues and signal degradation which are both key requirements for practitioners of ASR. Includes contributions from top ASR researchers from leading research units in the field

Deep Learning for NLP and Speech Recognition

Uday Kamath,John Liu,James Whitaker
Deep Learning for NLP and Speech Recognition Book PDF

Author : Uday Kamath,John Liu,James Whitaker
Publisher : Springer
Page : 621 pages
File Size : 50,7 Mb
Release : 2019-06-10
Category : Computers
ISBN : 9783030145965

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Deep Learning for NLP and Speech Recognition by Uday Kamath,John Liu,James Whitaker Pdf

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.

Visual Speech Recognition: Lip Segmentation and Mapping

Liew, Alan Wee-Chung,Wang, Shilin
Visual Speech Recognition Lip Segmentation and Mapping Book PDF

Author : Liew, Alan Wee-Chung,Wang, Shilin
Publisher : IGI Global
Page : 572 pages
File Size : 52,7 Mb
Release : 2009-01-31
Category : Computers
ISBN : 9781605661872

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Visual Speech Recognition: Lip Segmentation and Mapping by Liew, Alan Wee-Chung,Wang, Shilin Pdf

"This book introduces the readers to the various aspects of visual speech recognitions, including lip segmentation from video sequence, lip feature extraction and modeling, feature fusion and classifier design for visual speech recognition and speaker verification" résumé de l'éditeur.

Machine Learning and Knowledge Discovery in Databases

Hendrik Blockeel,Kristian Kersting,Siegfried Nijssen,Filip Železný
Machine Learning and Knowledge Discovery in Databases Book PDF

Author : Hendrik Blockeel,Kristian Kersting,Siegfried Nijssen,Filip Železný
Publisher : Springer
Page : 731 pages
File Size : 54,9 Mb
Release : 2013-08-28
Category : Computers
ISBN : 9783642409943

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Machine Learning and Knowledge Discovery in Databases by Hendrik Blockeel,Kristian Kersting,Siegfried Nijssen,Filip Železný Pdf

This three-volume set LNAI 8188, 8189 and 8190 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2013, held in Prague, Czech Republic, in September 2013. The 111 revised research papers presented together with 5 invited talks were carefully reviewed and selected from 447 submissions. The papers are organized in topical sections on reinforcement learning; Markov decision processes; active learning and optimization; learning from sequences; time series and spatio-temporal data; data streams; graphs and networks; social network analysis; natural language processing and information extraction; ranking and recommender systems; matrix and tensor analysis; structured output prediction, multi-label and multi-task learning; transfer learning; bayesian learning; graphical models; nearest-neighbor methods; ensembles; statistical learning; semi-supervised learning; unsupervised learning; subgroup discovery, outlier detection and anomaly detection; privacy and security; evaluation; applications; and medical applications.

Information Systems for Indian Languages

Chandan Singh,Gurpreet Singh Lehal,Jyotsna Sengupta,Dharam Veer Sharma,Vishal Goyal
Information Systems for Indian Languages Book PDF

Author : Chandan Singh,Gurpreet Singh Lehal,Jyotsna Sengupta,Dharam Veer Sharma,Vishal Goyal
Publisher : Springer
Page : 318 pages
File Size : 45,8 Mb
Release : 2011-02-11
Category : Computers
ISBN : 9783642194030

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Information Systems for Indian Languages by Chandan Singh,Gurpreet Singh Lehal,Jyotsna Sengupta,Dharam Veer Sharma,Vishal Goyal Pdf

This book constitutes the refereed proceedings of the International Conference on Information Systems for Indian Languages, ICISIL 2011, held in Patiala, India, in March 2011. The 63 revised papers presented were carefully reviewed and selected from 126 paper submissions (full papers as well as poster papers) and 25 demo submissions. The papers address all current aspects on localization, e-governance, Web content accessibility, search engine and information retrieval systems, online and offline OCR, handwriting recognition, machine translation and transliteration, and text-to-speech and speech recognition - all with a particular focus on Indic scripts and languages.

Speech, Image, and Language Processing for Human Computer Interaction: Multi-Modal Advancements

Tiwary, Uma Shanker
Speech Image and Language Processing for Human Computer Interaction Multi Modal Advancements Book PDF

Author : Tiwary, Uma Shanker
Publisher : IGI Global
Page : 387 pages
File Size : 45,8 Mb
Release : 2012-04-30
Category : Computers
ISBN : 9781466609556

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Speech, Image, and Language Processing for Human Computer Interaction: Multi-Modal Advancements by Tiwary, Uma Shanker Pdf

"This book identifies the emerging research areas in Human Computer Interaction and discusses the current state of the art in these areas"--Provided by publisher.

Pattern Recognition in Speech and Language Processing

Wu Chou,Biing-Hwang Juang
Pattern Recognition in Speech and Language Processing Book PDF

Author : Wu Chou,Biing-Hwang Juang
Publisher : CRC Press
Page : 413 pages
File Size : 50,7 Mb
Release : 2003-02-26
Category : Technology & Engineering
ISBN : 9780203010525

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Pattern Recognition in Speech and Language Processing by Wu Chou,Biing-Hwang Juang Pdf

Over the last 20 years, approaches to designing speech and language processing algorithms have moved from methods based on linguistics and speech science to data-driven pattern recognition techniques. These techniques have been the focus of intense, fast-moving research and have contributed to significant advances in this field. Pattern Reco