Automatic Speech and Speaker Recognition: Large Margin and Kernel Methods

Keshet, Joseph [Editor]; Bengio, Samy [Editor];

ISBN 10: 0470696834 / ISBN 13: 9780470696835

Edité par Wiley, 2009

A propos de cet article

Ex-library book with typical stickers and stampings. Priority Mail is available on this item. No international shipping.

N° de réf. du vendeur B180726aimug176566

Détails bibliographiques

• Titre: Automatic Speech and Speaker Recognition: Large Margin and Kernel Methods
• Éditeur: Wiley
• Date de parution: 2009
• Langue: anglais
• ISBN à 10 chiffres: 0470696834
• ISBN à 13 chiffres: 9780470696835
• Reliure: hardcover
• État de l'article: Good
• Poids: 21 onces

A propos de ce titre

Synopsis

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.

À propos de l'auteur

Dr Joseph Keshet, IDIAP, Switzerland
Dr Keshet received his B.Sc. and M.Sc. in electrical engineering from the Tel-Aviv University, Tel-Aviv, Israel, in 1994 and 2002, respectively. He got his Ph.D. from the Hebrew University of Jerusalem, Israel in 2007. From 1994 to 2002, he was with the Israeli Defense Forces (Intelligence Corps), where he was in charge of advanced research activities in the fields of speech coding. Since 2007, he is a research scientist in speech recognition at IDIAP Research Institute, Martigny, Switzerland.

Dr Samy Bengio, Google, California, US
Dr Bengio received his M.Sc. and Ph.D. degrees in Computer Science from University of Montreal in 1989 and 1993 respectively. Between 1999 and 2006, he was a senior researcher in statistical machine learning at IDIAP Research Institute, where he supervised PhD students and postdoctoral fellows working on many areas of machine learning. He is the author/co-author of more than 160 international publications, including 30 journal papers. He has organized several international workshops (such as the MLMI series) and been in the organization committee of several well known conferences (such as NIPS). Since early 2007, he is a research scientist in machine learning at Google, in Mountain View, California.