Impact of gender and emotion type in dialogue emotion recognition

Author

Chenchah, Farah ; Lachiri, Zied

Author_Institution

LR-SITI Lab., Nat. Inst. of Appl. Sci. & Technol., Tunis, Tunisia

fYear

2014

fDate

17-19 March 2014

Firstpage

464

Lastpage

467

Abstract

This paper presents a dialogue emotion recognition system using Hidden Markov Model (HMM). We have compared accuracy of Mel-frequency cepstral coefficients (MFCC), Energy, and wavelet sub-band energies and their first derivative and all possible combination. Based on our experiment, MFCC show better performance in comparison with the other studied features. We have also evaluated the impact of gender and emotion states on emotion detection. Experimental results show that a significant difference is observed depending on the type of emotion studied but also in the gender of the evaluated person.

Keywords

cepstral analysis; emotion recognition; gender issues; hidden Markov models; wavelet transforms; HMM; MFCC; dialogue emotion recognition system; emotion detection; emotion type; gender type; hidden Markov model; mel-frequency cepstral coefficients; wavelet sub-band energy; Accuracy; Emotion recognition; Feature extraction; Hidden Markov models; Mel frequency cepstral coefficient; Speech; Speech recognition; Hidden Markov Model (HMM); Mel-Frequency Cepstral Coefficients (MFCC); dependant context; emotion recognition; feature extraction;

fLanguage

English

Publisher

ieee

Conference_Titel

Advanced Technologies for Signal and Image Processing (ATSIP), 2014 1st International Conference on

Conference_Location

Sousse

Type

conf

DOI

10.1109/ATSIP.2014.6834656

Filename

6834656