Recognizing emotion in speech

Author

Dellaert, Frank ; Polzin, Thomas ; Waibel, Alex

Author_Institution

Sch. of Comput. Sci., Carnegie Mellon Univ., Pittsburgh, PA, USA

Volume

3

fYear

1996

fDate

3-6 Oct 1996

Firstpage

1970

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Spoken Language, 1996. ICSLP 96. Proceedings., Fourth International Conference on

Conference_Location

Philadelphia, PA

Print_ISBN

0-7803-3555-4

Type

conf

DOI

10.1109/ICSLP.1996.608022

Filename

608022