A Study of Deep Belief Network Based Chinese Speech Emotion Recognition

Author

Bu Chen ; Qian Yin ; Ping Guo

Author_Institution

Image Process. & Pattern Recognition Lab., Beijing Normal Univ., Beijing, China

fYear

2014

fDate

15-16 Nov. 2014

Firstpage

180

Lastpage

184

Abstract

This paper presents a deep learning method application to the extraction of emotions included in Chinese speech with a deep belief network (DBN) structure. Eight proper features such as pitch, mel frequency cepstrum coefficient (MFCC) are chosen from Mandarin speech used as network inputs, and a DBN classifier is used instead of traditional shallow learning methods to recognition of emotions. Experiment studies have proven that its recognition rate is higher than that of the traditional back propagation (BP) method and support vector machine (SVM) classifier.

Keywords

Boltzmann machines; emotion recognition; feature extraction; learning (artificial intelligence); signal classification; speech recognition; Chinese speech emotion recognition; DBN classifier; DBN structure; MFCC feature; Mandarin speech; deep learning method application; deep-belief network; emotion extraction; emotion recognition rate; mel frequency cepstrum coefficient feature; network inputs; pitch feature; Emotion recognition; Feature extraction; Speech; Speech processing; Speech recognition; Support vector machines; Training; Chinese speech emotion recognition; DBN; deep learning; feature extraction;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Intelligence and Security (CIS), 2014 Tenth International Conference on

Conference_Location

Kunming

Print_ISBN

978-1-4799-7433-7

Type

conf

DOI

10.1109/CIS.2014.148

Filename

7016878