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

Nonnegative Tensor PCA and Application to Speaker Recognition in Noise Environments

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)
Qiang Wu ; Dept. of Comput. Sci., Shanghai Jiao Tong Univ., Shanghai ; Liqing Zhang

In this paper a new approach called nonnegative tensor principal component analysis (NTPCA) with sparse constraint is proposed for speech feature extraction. We encode speech as a general higher order tensor in order to extract the robust feature from multiple interrelated feature subspace. First, speech signals are represented by cochleagram based on frequency selectivity at basilar membrane and inner hair cells; Then, a low dimension sparse representation based on tensor structure is extracted by NTPCA for robust speaker modeling. Alternating projection algorithm is used to obtain a stable solution and makes sure the useful information of each subspace in the higher order tensor being preserved. Experiment results demonstrate that our method can increase the recognition accuracy specifically in noise environments.

Published in:

Natural Computation, 2008. ICNC '08. Fourth International Conference on  (Volume:4 )

Date of Conference:

18-20 Oct. 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.