Article Code : 139511231511464967(DOI : 10.7508/jist.2017.17.007)

Article Title : Speech Emotion Recognition Based on Fusion Method

Journal Number : 17 Winter 2017

Visited : 287

Files : 451 KB

List of Authors

  Full Name Email Grade Degree Corresponding Author
1 Sara Motamed Teacher Assistant PhD
2 Saeed Setayeshi Professor PhD
3 Azam Rabiee - -
4 Arash Sharifi - -


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.