By Topic

Speech emotion recognition combining acoustic features and linguistic information in a hybrid support vector machine-belief network architecture

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)
Schuller, B. ; Inst. for Human-Comput. Commun., Technische Univ. Munchen, Germany ; Rigoll, G. ; Lang, M.

In this paper we introduce a novel approach to the combination of acoustic features and language information for a most robust automatic recognition of a speaker's emotion. Seven discrete emotional states are classified throughout the work. Firstly a model for the recognition of emotion by acoustic features is presented. The derived features of the signal-, pitch-, energy, and spectral contours are ranked by their quantitative contribution to the estimation of an emotion. Several different classification methods including linear classifiers, Gaussian mixture models, neural nets, and support vector machines are compared by their performance within this task. Secondly an approach to emotion recognition by the spoken content is introduced applying belief network based spotting for emotional key-phrases. Finally the two information sources are integrated in a soft decision fusion by using a neural net. The gain is evaluated and compared to other advances. Two emotional speech corpora used for training and evaluation are described in detail and the results achieved applying the propagated novel advance to speaker emotion recognition are presented and discussed.

Published in:

Acoustics, Speech, and Signal Processing, 2004. Proceedings. (ICASSP '04). IEEE International Conference on  (Volume:1 )

Date of Conference:

17-21 May 2004