Recognizing Emotion in Speech

Abstract

This paper explores several statistical pattern recognition techniques to classify utterances according to their emotional content. We have recorded a corpus containing emotional speech with over a 1000 utterances from different speakers. We present a new method of extracting prosodic features from speech, based on a smoothing spline approximation of the pitch contour. To make maximal use of the limited amount of training data available, we introduce a novel pattern recognition technique: majority voting of subspace specialists. Using this technique, we obtain classification performance that is close to human performance on the task.