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Study Automatic Speech and Speaker Recognition: Large Margin and Kernel Methods

Posted on 2010-03-15




Name:Study Automatic Speech and Speaker Recognition: Large Margin and Kernel Methods
ASIN/ISBN:0470696834
Language:English
File size:2.15 Mb
Publish Date: 2009-03-23
ISBN: 0470696834
Pages: 272 pages
File Type: PDF
File Size: 2,15 MB
Other Info: Wiley
   Study Automatic Speech and Speaker Recognition: Large Margin and Kernel Methods

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Joseph Keshet, Samy Bengio, “Automatic Speech and Speaker Recognition: Large Margin and Kernel Methods”

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.

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