A Study of Deep Belief Network Based Chinese Speech Emotion Recognition

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...

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Bibliographic Details
Published in2014 Tenth International Conference on Computational Intelligence and Security pp. 180 - 184
Main Authors Bu Chen, Qian Yin, Ping Guo
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.11.2014
Subjects
Chinese speech emotion recognition
DBN
deep learning
Emotion recognition
Feature extraction
Speech
Speech processing
Speech recognition
Support vector machines
Training
Online AccessGet full text
DOI10.1109/CIS.2014.148

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Summary: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.
DOI:10.1109/CIS.2014.148