Speech Emotion Recognition Based on Fusion Method
Subject Areas : Speech Processing
Sara
Motamed
1
(Assistant Professor, Department of Computer, Fouman & Shaft Unit, Islamic Azad University, Fuman, Iran)
Saeed
Setayeshi
2
(Amirkabir University)
Azam
Rabiee
3
(Islamic Azad University Isfahan)
Arash
Sharifi
4
(Science and Research Branch, Islamic Azad University, Tehran, Iran)
Keywords: Speech Emotion Recognition , Mel Frequency Cepstral Coefficient (MFCC) , Fixed and Variable Structures Stochastic Automata , Multi-constraint, Fusion Method,
Abstract :
Speech emotion signals are the quickest and most neutral method in individuals’ relationships, leading researchers to develop speech emotion signal as a quick and efficient technique to communicate between man and machine. This paper introduces a new classification method using multi-constraints partitioning approach on emotional speech signals. To classify the rate of speech emotion signals, the features vectors are extracted using Mel frequency Cepstrum coefficient (MFCC) and auto correlation function coefficient (ACFC) and a combination of these two models. This study found the way that features’ number and fusion method can impress in the rate of emotional speech recognition. The proposed model has been compared with MLP model of recognition. Results revealed that the proposed algorithm has a powerful capability to identify and explore human emotion.
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