Scheduled System Maintenance:
On Wednesday, July 29th, IEEE Xplore will undergo scheduled maintenance from 7:00-9:00 AM ET (11:00-13:00 UTC). During this time there may be intermittent impact on performance. We apologize for any inconvenience.
By Topic

Spoken emotion recognition using kernel discriminant locally linear embedding

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 $31
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)
Zhang, S. ; Sch. of Commun. & Inf. Eng., Univ. of Electron. Sci. & Technol. of China, Chengdu, China ; Li, L. ; Zhao, Z.

A new kernel-based manifold learning algorithm, called kernel discriminant locally linear embedding (KDLLE), is presented for spoken emotion recognition. KDLLE aims to make the interclass dissimilarity definitely larger than the intraclass dissimilarity in a reproducing kernel Hilbert space for the purpose of nonlinearly extracting the low-dimensional discriminant embedded data representations with striking performance improvement in spoken emotion recognition. Experimental results on the Berlin speech corpus demonstrate the effectiveness of KDLLE.

Published in:

Electronics Letters  (Volume:46 ,  Issue: 19 )