Discriminative Learning for Speech Recognition: Theory and Practice

Xiaodong He; Li Deng

ISBN 10: 1598293087 / ISBN 13: 9781598293081

Edité par Morgan & Claypool, 2008

A propos de cet article

Ex-library with the usual features. Library label on front cover. The interior is clean and tight. Binding is good. Cover shows light wear.

N° de réf. du vendeur 122061

Détails bibliographiques

• Titre: Discriminative Learning for Speech Recognition: Theory and Practice
• Éditeur: Morgan & Claypool
• Date de parution: 2008
• Langue: anglais
• ISBN à 10 chiffres: 1598293087
• ISBN à 13 chiffres: 9781598293081
• Reliure: Soft Cover
• État de l'article: Good
• Type de livre: Ex-Library

A propos de ce titre

Synopsis

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