Cart (Loading....) | Create Account
Close category search window
 

Rapid Speaker Adaptation Using Clustered Maximum-Likelihood Linear Basis With Sparse Training Data

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)
Yun Tang ; McGill Univ., Montreal ; Rose, R.

Speaker space-based adaptation methods for automatic speech recognition have been shown to provide significant performance improvements for tasks where only a few seconds of adaptation speech is available. However, these techniques are not widely used in practical applications because they require large amounts of speaker-dependent training data and large amounts of computer memory. The authors propose a robust, low-complexity technique within this general class that has been shown to reduce word error rate, reduce the large storage requirements associated with speaker space approaches, and eliminate the need for large numbers of utterances per speaker in training. The technique is based on representing speakers as a linear combination of clustered linear basis vectors and a procedure is presented for maximum-likelihood estimation of these vectors from training data. Significant word error rate reduction was obtained using these methods relative to speaker independent performance for the Resource Management and Wall Street Journal task domains.

Published in:

Audio, Speech, and Language Processing, IEEE Transactions on  (Volume:16 ,  Issue: 3 )

Date of Publication:

March 2008

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.