[1] Wang Qingyun, Zhao Li, Liang Ruiyu, et al. Annoyance-type speech emotion detectionin working environment [J]. Journal of Southeast University (English Edition), 2013, 29 (4): 366-371. [doi:10.3969/j.issn.1003-7985.2013.04.003]

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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

29

Issue:

2013 4

Page:

366-371

Research Field:

Information and Communication Engineering

Publishing date:

2013-12-20

Info

Title:

Annoyance-type speech emotion detectionin working environment

Author(s):

Wang Qingyun¹;  2;  Zhao Li¹;  Liang Ruiyu¹;  Zhang Xiaodan¹

¹School of Information Science and Engineering, Southeast University, Nanjing 210096, China
²School of Communication Engineering, Nanjing Institute of Technology, Nanjing 211167, China

Keywords:

speech emotion detection;  annoyance type;  sentence length;  shuffled frog leaping algorithm

PACS:

TN912.3

DOI:

10.3969/j.issn.1003-7985.2013.04.003

Abstract:

In order to recognize people’s annoyance emotions in the working environment and evaluate emotional well-being, emotional speech in a work environment is induced to obtain adequate samples of emotional speech, and a Mandarin database with two thousands samples is built. In searching for annoyance-type emotion features, the prosodic feature and the voice quality feature parameters of the emotional statements are extracted first. Then an improved back propagation(BP)neural network based on the shuffled frog leaping algorithm(SFLA)is proposed to recognize the emotion. The recognition capability of the BP, radical basis function(RBF)and the SFLA neural networks are compared experimentally. The results show that the recognition ratio of the SFLA neural network is 4.7% better than that of the BP neural network and 4.3% better than that of the RBF neural network. The experimental results demonstrate that the random initial data trained by the SFLA can optimize the connection weights and thresholds of the neural network, speed up the convergence and improve the recognition rate.

References:

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Memo

Memo:

Biography: Wang Qingyun(1972—), female, doctor, associate professor, wangqingyun@vip.163.com.
Foundation items: The National Natural Science Foundation of China(No.61375028, 61301219), China Postdoctoral Science Foundation(No.2012M520973), the Scientific Research Funds of Nanjing Institute of Technology(No.ZKJ201202).
Citation: Wang Qingyun, Zhao Li, Liang Ruiyu, et al.Annoyance-type speech emotion detection in working environment[J].Journal of Southeast University(English Edition), 2013, 29(4):366-371.[doi:10.3969/j.issn.1003-7985.2013.04.003]

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