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

Feature reduction using PCA with multi-condition training for practical speech recognition systems

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)
Kaneda, Y. ; Grad. Sch. of Eng. Sci., Osaka Univ., Toyonaka, Japan ; Hayasaka, N. ; Iiguni, Y.

In this paper, we propose a new method to extract noise-robust features and reduce the number of them for developing a small-sized speech recognition system. Although it is assumed that no correlation between features occurs in typical recognition systems, high correlations occur in practical cases. In consideration of this point, we apply principal component analysis to typical features and reduce the correlations between them. Furthermore, we introduce multi-condition training to improve recognition performance under noisy environments. From a large amount of experiments, when the number of features was reduce by two-thirds, the proposed method allowed us to maintain high performance at assumed SNR levels. Finally, we consider the relation between recognition performance and the amount of information when we use the proposed features.

Published in:

Communications and Information Technologies (ISCIT), 2012 International Symposium on

Date of Conference:

2-5 Oct. 2012

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.