This paper explores several statistical panem recognition techniques to classify utterances according to their emotional content. We have recorded a corpus containing emotional speech with over a lo00 utterances from different speaka. We present a new method of extracting prosodic featurrs 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 pattem recognition technique: majority voting of subspace specialists. Using this technique, we obtain classification perfonnance that is close to human performance on the task.
REF extracted prosodic features from speech and applied majority voting of subspace specialists.
Recognizing emotion in speech
Proceeding of Fourth International Conference on Spoken Language Processing. ICSLP '96
Proceeding of Fourth International Conference on Spoken Language Processing. ICSLP '96
Computer Science