Title :
Recognizing emotion in speech
Author :
Dellaert, Frank ; Polzin, Thomas ; Waibel, Alex
Author_Institution :
Sch. of Comput. Sci., Carnegie Mellon Univ., Pittsburgh, PA, USA
Abstract :
The paper explores several statistical pattern recognition techniques to classify utterances according to their emotional content. The authors have recorded a corpus containing emotional speech with over a 1000 utterances from different speakers. They 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, they introduce a novel pattern recognition technique: majority voting of subspace specialists. Using this technique, they obtain classification performance that is close to human performance on the task
Keywords :
human factors; pattern classification; psychology; smoothing methods; speech recognition; splines (mathematics); statistical analysis; classification performance; emotion recognition; emotional content; emotional speech corpus; majority voting; pitch contour; prosodic feature extraction; smoothing spline approximation; speech; statistical pattern recognition techniques; subspace specialists; training data; utterance classification; Data mining; Emotion recognition; Feature extraction; Humans; Pattern recognition; Smoothing methods; Speech recognition; Spline; Training data; Voting;
Conference_Titel :
Spoken Language, 1996. ICSLP 96. Proceedings., Fourth International Conference on
Conference_Location :
Philadelphia, PA
Print_ISBN :
0-7803-3555-4
DOI :
10.1109/ICSLP.1996.608022
