你好! Shipping to Taiwan with premium packaging for just NT$300 

Ship to
Taiwan
0

My Account

  • argentina
  • chile
  • colombia
  • españa
  • méxico
  • perú
  • estados unidos
  • internacional

menu

Select your country

Americas

Europe

Rest of the world

portada Discriminative Learning for Speech Recognition: Theory and Practice
Type
Physical Book
Author
Publisher
Language
English
Pages
112
Format
Paperback
Dimensions
23.5x19.1x0.7 cm
Weight
0.23 kg.
ISBN13
9783031014291
Edition No.
1
Categories

Discriminative Learning for Speech Recognition: Theory and Practice

Li Deng (Author) · Xiadong He (Author) · Springer · Paperback

Discriminative Learning for Speech Recognition: Theory and Practice - He, Xiadong ; Deng, Li

Cheaper New Book Imported to Taiwan
Delivery: 04 Dec - 14 Dec Shipping: 15 to 16 business days.
NT$ 1,307
Faster New Book Imported to Taiwan
Delivery: 20 Nov - 30 Nov Shipping: 5 to 6 business days.
NT$ 1,569
NT$ 1,307

Choose the list to add your product or create one New List

Go to My Wishlists
Calculate shipping cost

Synopsis "Discriminative Learning for Speech Recognition: Theory and Practice"

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

Customers reviews

More customer reviews
  • 0% (0)
  • 0% (0)
  • 0% (0)
  • 0% (0)
  • 0% (0)

Frequently Asked Questions about the Book

Is the book original?

All books in our catalog are Original.

In what language is the book written?

The book is written in English.

What is the format of this book?

The binding of this edition is Paperback.

Questions and Answers about the Book

Do you have a question about the book? Login to be able to add your own question.

Opinions about Bookdelivery

More customer reviews