By Topic

Recuperating spectral features using glottal information and its application to speaker recognition

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)
Pu Yang ; Coll. of Comput. Sci. & Technol., Zhejiang Univ., Hangzhou, China ; Yingchun Yang ; Wu, Zhaohui

Most state-of-the-art speaker recognition systems do improve their performance when utilizing glottal information. Although they successfully model its changes as features for recognition task, they do not take into account the spectral variations caused by it. A method that can lessen this influence, using both long-term and short-term glottal information, is proposed. Spectral features behave more discriminative in text-independent automatic speaker recognition (ASR) through this recuperation. Our method was applied to YOHO corpus and our SRMC corpus. The experimental works show promising results.

Published in:

Neural Networks, 2004. Proceedings. 2004 IEEE International Joint Conference on  (Volume:4 )

Date of Conference:

25-29 July 2004