Automatic Speech and Speaker Recognition: Large Margin and Kernel Methods
Abstract
This is the first book dedicated to uniting research related to speech and speaker
recognition based on the recent advances in large margin and kernel methods. The
first part of the book presents theoretical and practical foundations of large
margin and kernel methods, from support vector machines to large margin methods for
structured learning. The second part of the book is dedicated to acoustic modeling
of continuous speech recognizers, where the grounds for practical large margin
sequence learning are set. The third part introduces large margin methods for
discriminative language modeling. The last part of the book is dedicated to the
application of keyword spotting, speaker verification and spectral clustering. The
book is an important reference to researchers and practitioners in the field of
modern speech and speaker recognition. The purpose of the book is twofold; first,
to set the theoretical foundation of large margin and kernel methods relevant to
speech recognition domain; second, to propose a practical guide on implementation
of these methods to the speech recognition domain. The reader is presumed to have
basic knowledge of large margin and kernel methods and of basic algorithms in
speech and speaker recognition.
