Abstract

The need for automatic recognition of a speaker's emotion within a spoken dialog system framework has received increased attention with demand for computer interfaces that provide natural and user-adaptive spoken interaction. This paper addresses the problem of automatically recognizing a child's emotional state using information obtained from audio and video signals. The study is based on a multimodal data corpus consisting of spontaneous conversations between a child and a computer agent. Four different techniques-- k-nearest neighborhood (k-NN) classifier, decision tree, linear discriminant classifier (LDC), and support vector machine classifier (SVC)-- were employed for classifying utterances into 2 emotion classes, negative and non-negative, for both acoustic and visual information. Experimental results show that, overall, combining visual information with acoustic information leads to performance …

Date

2009

Authors

Serdar Yildirim, Shrikanth Narayanan

Book

Proceedings of the 2nd Workshop on Child, Computer and Interaction

Pages

1-4