Automatic Spoken Affect Analysis and Classification

This paper reports results from early experiments on automatic classification of spoken affect. The task was to classify short spoken sentences into one of two affect classes: approving or disapproving. Using an optimal combination of six acoustic measurements our classifier achieved an accuracy of 65% to 88% for speaker dependent, text-independent classification. The results suggest that pitch and energy measurements may be used to automatically classify spoken affect but more research will be necessary to understand individual variations and how to broaden the range of affect classes which can be recognized. In a second experiment we compared human performance in classifying the same speech samples. We found similarities between human and automatic classification results.

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