Automatic recognition of negative emotion in speech using support vector machine

Abstract

This paper addresses negative emotion recognition using paralinguistic information in speech for speech dialogue system. Speech conveys not only linguistic information but also paralinguistic and non-linguistic information such as the emotions, attitudes, and intentions. This easily perceivable information plays a key role in a spoken dialog system. However, most of previous speech recognition systems fail to consider this significant information, focusing only on linguistic information, thus hindering the development of more natural speech dialog systems. In order to utilize these significant information for speech dialog systems, this paper focuses on negative emotion recognition from Japanese utterances. 6552-dimensional acoustic features were extracted from 6300 Japanese utterances of 50 people in three emotional state: negative; positive; and neutral. Negative emotion includes anger, sad and dislike. While positive emotion includes favor, joy, and relief. They were classified by SVM and evaluated by a 10-fold cross validation. The experimental result showed the recognition rate of 93.4 % for the classification of negative and positive and 95.0% for the classification of negative and neutral.