Impact of Gender and Emotion Type in Dialogue Emotion Recognition

被引：0

作者：

Chenchah, Farah ^{[1
]}

Lachiri, Zied ^{[1
]}

机构：

[1] Natl Inst Appl Sci & Technol, LR SITI Lab, Tunis, Tunisia

来源：

2014 1ST INTERNATIONAL CONFERENCE ON ADVANCED TECHNOLOGIES FOR SIGNAL AND IMAGE PROCESSING (ATSIP 2014) | 2014年

关键词：

Mel-Frequency Cepstral Coefficients (MFCC); Hidden Markov Model (HMM); feature extraction; emotion recognition; dependant context; FEATURES;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

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.

引用

页码：464 / 467

页数：4

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