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This book discusses large margin and kernel methods for speech and speaker
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.