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![]( "Buchcover ISBN 9780470742037")
Automatic Speech and Speaker Recognition
Large Margin and Kernel Methods
herausgegeben von Joseph Keshet und Samy BengioThis book discusses large margin and kernel methods for speechand speaker recognition
Speech and Speaker Recognition: Large Margin and KernelMethods is a collation of research in the recent advances inlarge margin and kernel methods, as applied to the field of speechand speaker recognition. It presents theoretical and practicalfoundations of these methods, from support vector machines to largemargin methods for structured learning. It also provides examplesof large margin based acoustic modelling for continuous speechrecognizers, where the grounds for practical large margin sequencelearning are set. Large margin methods for discriminative languagemodelling and text independent speaker verification are alsoaddressed in this book.
Key Features:
* Provides an up-to-date snapshot of the current state ofresearch in this field
* Covers important aspects of extending the binary support vectormachine to speech and speaker recognition applications
* Discusses large margin and kernel method algorithms forsequence prediction required for acoustic modeling
* Reviews past and present work on discriminative training oflanguage models, and describes different large margin algorithmsfor the application of part-of-speech tagging
* Surveys recent work on the use of kernel approaches totext-independent speaker verification, and introduces the mainconcepts and algorithms
* Surveys recent work on kernel approaches to learning asimilarity matrix from data
This book will be of interest to researchers, practitioners, engineers, and scientists in speech processing and machine learningfields.
Speech and Speaker Recognition: Large Margin and KernelMethods is a collation of research in the recent advances inlarge margin and kernel methods, as applied to the field of speechand speaker recognition. It presents theoretical and practicalfoundations of these methods, from support vector machines to largemargin methods for structured learning. It also provides examplesof large margin based acoustic modelling for continuous speechrecognizers, where the grounds for practical large margin sequencelearning are set. Large margin methods for discriminative languagemodelling and text independent speaker verification are alsoaddressed in this book.
Key Features:
* Provides an up-to-date snapshot of the current state ofresearch in this field
* Covers important aspects of extending the binary support vectormachine to speech and speaker recognition applications
* Discusses large margin and kernel method algorithms forsequence prediction required for acoustic modeling
* Reviews past and present work on discriminative training oflanguage models, and describes different large margin algorithmsfor the application of part-of-speech tagging
* Surveys recent work on the use of kernel approaches totext-independent speaker verification, and introduces the mainconcepts and algorithms
* Surveys recent work on kernel approaches to learning asimilarity matrix from data
This book will be of interest to researchers, practitioners, engineers, and scientists in speech processing and machine learningfields.
