By Topic

A fast segmental clustering approach to decision tree tying based acoustic modeling

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

2 Author(s)
Reichl, W. ; AT&T Bell Labs., Murray Hill, NJ, USA ; Chou, W.

A fast two level segmental clustering approach to decision tree based state tying is proposed for large vocabulary speech recognition. This approach extends the conventional segmental K-means approach to phonetic decision tree tying based acoustic modeling. It achieves high recognition performances while reducing the model training time from days to hours, compared to approaches based on incremental Baum-Welch training. Experimental results for this fast segmental clustering approach are presented for resource management and the Wall Street Journal tasks

Published in:

Automatic Speech Recognition and Understanding, 1997. Proceedings., 1997 IEEE Workshop on

Date of Conference:

14-17 Dec 1997