Automatic Speech Emotion Recognition using Auditory Models with Binary Decision Tree and SVM

Date

2014-08-28

Author

Yuncu, Enes
Hacıhabiboğlu, Hüseyin
Bozsahin, Cem

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Affective computing is a term for the design and development of algorithms that enable computers to recognize the emotions of their users and respond in a natural way. Speech, along with facial gestures, is one of the primary modalities with which humans express their emotions. While emotional cues in speech are available to an interlocutor in a dyadic conversation setting, their subjective recognition is far from accurate. This is due to the human auditory system which is primarily non-linear and adaptive. An automatic speech emotion recognition algorithm based on a computational model of the human auditory system is described in this paper. The devised system is tested on three emotional speech datasets. The results of a subjective recognition task is also reported. It is shown that the proposed algorithm provides recognition rates that are comparable to those of human raters.

Subject Keywords

Speech, Speech recognition, Emotion recognition, Filter banks, Databases , Feature extraction, Modulation

URI

https://hdl.handle.net/11511/32503

DOI

https://doi.org/10.1109/icpr.2014.143

Collections

Graduate School of Informatics, Conference / Seminar

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Citation Formats

E. Yuncu, H. Hacıhabiboğlu, and C. Bozsahin, “Automatic Speech Emotion Recognition using Auditory Models with Binary Decision Tree and SVM,” 2014, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/32503.