Automatic Speech and Speaker Recognition (PDF)

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.