By Topic

Maximum mutual information training of a neural predictive-based HMM speech recognition system

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Hassanein, K. ; Dept. of Electr. & Comput. Eng., Waterloo Univ. Ont., Canada ; Deng, L. ; Elmasry, M.

A corrective training scheme based on the maximum mutual information (MMI) criterion is developed for training a neural predictive-based HMM (hidden Markov model) speech recognition system. The performance of the system on speech recognition tasks when trained with this technique is compared to its performance when trained using the maximum likelihood (ML) criterion. Preliminary results obtained indicate the superiority of ML training over MMI training for predictive-based models. This result is in agreement with earlier findings in the literature regarding direct classification models

Published in:

Neural Networks for Signal Processing [1992] II., Proceedings of the 1992 IEEE-SP Workshop

Date of Conference:

31 Aug-2 Sep 1992