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Discriminative Learning for Speech Recognition - Theory and Practice (Paperback)
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Discriminative Learning for Speech Recognition - Theory and Practice (Paperback)
Series: Synthesis Lectures on Speech and Audio Processing
Expected to ship within 10 - 15 working days
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In this book, we introduce the background and mainstream methods of
probabilistic modeling and discriminative parameter optimization
for speech recognition. The specific models treated in depth
include the widely used exponential-family distributions and the
hidden Markov model. A detailed study is presented on unifying the
common objective functions for discriminative learning in speech
recognition, namely maximum mutual information (MMI), minimum
classification error, and minimum phone/word error. The unification
is presented, with rigorous mathematical analysis, in a common
rational-function form. This common form enables the use of the
growth transformation (or extended Baum-Welch) optimization
framework in discriminative learning of model parameters. In
addition to all the necessary introduction of the background and
tutorial material on the subject, we also included technical
details on the derivation of the parameter optimization formulas
for exponential-family distributions, discrete hidden Markov models
(HMMs), and continuous-density HMMs in discriminative learning.
Selected experimental results obtained by the authors in firsthand
are presented to show that discriminative learning can lead to
superior speech recognition performance over conventional parameter
learning. Details on major algorithmic implementation issues with
practical significance are provided to enable the practitioners to
directly reproduce the theory in the earlier part of the book into
engineering practice. Table of Contents: Introduction and
Background / Statistical Speech Recognition: A Tutorial /
Discriminative Learning: A Unified Objective Function /
Discriminative Learning Algorithm for Exponential-Family
Distributions / Discriminative Learning Algorithm for Hidden Markov
Model / Practical Implementation of Discriminative Learning /
Selected Experimental Results / Epilogue / Major Symbols Used in
the Book and Their Descriptions / Mathematical Notation /
Bibliography
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