Emotional speech classification in consensus building

Ning He, Shuoqing Yao, Osamu Yoshie

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)


In this paper we introduce a novel approach that robust automatic speech features recognition of one's emotion is achieved in a classification model named decision forest. The 13th order of Mel-frequency ceptstrum coefficients (MFCC) vector is processed as the multivariate data that will be imported to our classifier. In order to draw underlying and inductive information behind the MFCC feature, our decision forest classifier contains two stages to make classification, a supervised clustering based pattern extraction stage and a soft discretization based decision forest stage. Finally, a Japanese emotion corpus used for training and evaluation is described in detail. The results in recognition of six discrete emotions exceeded a mean value of 81% recognition rate.

Original languageEnglish
Title of host publication2014 10th International Conference on Communications, COMM 2014 - Conference Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Print)9781479923854
Publication statusPublished - 2014
Event2014 10th International Conference on Communications, COMM 2014 - Bucharest, Romania
Duration: 2014 May 292014 May 31

Publication series

NameIEEE International Conference on Communications
ISSN (Print)1550-3607


Conference2014 10th International Conference on Communications, COMM 2014


  • MFCC
  • classification
  • consensus building
  • decision forest
  • speech emotion recognition

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Electrical and Electronic Engineering


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