Techniques for Noise Robustness in Automatic Speech Recognition
Tuomas Virtanen, Rita Singh, Bhiksha Raj (editors)
ISBN: 978-1-1199-7088-0
Publisher: Wiley

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

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Understanding Prosody: The Role of Context, Function and Communication

Ed. by Niebuhr, Oliver

Series:Language, Context and Cognition 13,   De Gruyter

http://www.degruyter.com/view/product/186201?format=G or http://linguistlist.org/pubs/books/get-book.cfm?BookID=63238

The volume represents a state-of-the-art snapshot of the research on prosody for phoneticians, linguists and speech technologists. It covers well-known models and languages. How are prosodies linked to speech sounds? What are the relations between prosody and grammar? What does speech perception tell us about prosody, particularly about the constituting elements of intonation and rhythm? The papers of the volume address questions like these with a special focus on how the notion of context-based coding, the knowledge of prosodic functions and the communicative embedding of prosodic elements can advance our understanding of prosody.

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 Albert Di Cristo: « La Prosodie de la Parole : Une Introduction », Editions de Boeck-Solal (296 p). 

Sommaire : 

Avant –propos, Introduction, ;

 Ch.1 : Eléments de définition ; 

 Ch 2. Situation de la prosodie dans le champ des sciences du langage et dans l’étude de la communication ; 

Ch 3. La prosodie sur les deux versants de la communication orale interindividuelle (production et compréhension) ; 

Ch 4. La prosodie et le cerveau ;

 Ch 5. La matérialité de la prosodie ; 

Ch 6. Les niveau d’analyse et de représentation de la prosodie ; 

Ch 7. Les théories, les modèles de la prosodie et leurs appareils formels ;

 Ch 8 La fonctionnalité plurielle de la prosodie ; 

Ch 9. Les relations de la prosodie avec les sens ; 

Epilogue. 

Suggestions de lecture ;

 Index des termes ; 

Index des noms propres.

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Björn Schuller, Anton Batliner Computational Paralinguistics: Emotion, Affect and Personality in Speech and Language Processing Wiley, ISBN: 978-1-119-97136-8, 344 pages, November 2013 Description - This book presents the methods, tools and techniques that are currently being used to recognise (automatically) the affect, emotion, personality and everything else beyond linguistics (‘paralinguistics’) expressed by or embedded in human speech and language. - It is the first book to provide such a systematic survey of paralinguistics in speech and language processing. The technology described has evolved mainly from automatic speech and speaker recognition and processing, but also takes into account recent developments within speech signal processing, machine intelligence and data mining. - Moreover, the book offers a hands-on approach by integrating actual data sets, software, and open-source utilities which will make the book invaluable as a teaching tool and similarly useful for those professionals already in the field. Key features: - Provides an integrated presentation of basic research (in phonetics/linguistics and humanities) with state-of-the-art engineering approaches for speech signal processing and machine intelligence. - Explains the history and state of the art of all of the sub-fields which contribute to the topic of computational paralinguistics. - Covers the signal processing and machine learning aspects of the actual computational modelling of emotion and personality and explains the detection process from corpus collection to feature extraction and from model testing to system integration. - Details aspects of real-world system integration including distribution, weakly supervised learning and confidence measures. - Outlines machine learning approaches including static, dynamic and context-sensitive algorithms for classification and regression. - Includes a tutorial on freely available toolkits, such as the open-source ‘openEAR’ toolkit for emotion and affect recognition co-developed by one of the authors, and a listing of standard databases and feature sets used in the field to allow for immediate experimentation enabling the reader to build an emotion detection model on an existing corpus. Links: - The book: http://eu.wiley.com/WileyCDA/WileyTitle/productCd-1119971365.html - Table of Contents (pdf): http://media.wiley.com/product_data/excerpt/65/11199713/1119971365-16.pdf - Chapter01 (pdf): http://media.wiley.com/product_data/excerpt/65/11199713/1119971365-14.pdf 

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Foundations and Trends in Signal Processing (www.nowpublishers.com/sig) has published the following issue:   

Volume 7, Issue 3-4                                                                                                                                                                   
Deep Learning: Methods and Applications                                                               
By Li Deng and Dong Yu (Microsoft Research, USA)       
http://dx.doi.org/10.1561/2000000039                                       

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