Abstract

This paper reports on methods for automatic classification of spoken utterances based on the emotional state of the speaker. The data set used for the analysis comes from a corpus of human-machine dialogues recorded from a commercial application deployed by SpeechWorks. Linear discriminant classification with Gaussian class-conditional probability distribution and k-nearest neighbors methods are used to classify utterances into two basic emotion states, negative and non-negative The features used by the classifiers are utterance-level statistics of the fundamental frequency and energy of the speech signal. To improve classification performance, two specific feature selection methods are used; namely, promising first selection and forward feature selection. Principal component analysis is used to reduce the dimensionality of the features while maximizing classification accuracy. Improvements obtained by …

Date

2001

Authors

Chul Min Lee, Shrikanth Narayanan, Roberto Pieraccini

Conference

IEEE Workshop on Automatic Speech Recognition and Understanding, 2001. ASRU'01.

Pages

240-243

Publisher

IEEE