Affective structure modeling of speech using probabilistic context free grammar for emotion recognition

Kun Yi Huang, Jia Kuan Lin, Yu Hsien Chiu, Chung Hsien Wu

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

2 Citations (Scopus)

Abstract

A complete emotional expression typically contains a complex temporal course in a natural conversation. Related research on utterance-level and segment-level processing lacks understanding of the underlying structure of emotional speech. In this study, a hierarchical affective structure of an emotional utterance characterized by the probabilistic context free grammars (PCFGs) is proposed for emotion modeling. SVM-based emotion profiles are obtained and employed to segment the utterance into emotionally consistent segments. Vector quantization is applied to convert the emotion profile of each segment into codewords. A binary tree in which each node represents a codeword is constructed to characterize the affective structure of the utterance modeled by PCFG. Given an input utterance, the output emotion is determined according to the PCFG-based emotion model with the highest likelihood of the speech segments along with the score of the affective structure. For evaluation, the EMO-DB database and its expansion in utterance length were conducted. Experimental results show that the proposed method achieved emotion recognition accuracy of 87.22% for long utterances and outperformed the SVM-based method.

Original languageEnglish
Title of host publication2015 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5286-5290
Number of pages5
ISBN (Electronic)9781467369978
DOIs
Publication statusPublished - 2015 Aug 4
Event40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015 - Brisbane, Australia
Duration: 2014 Apr 192014 Apr 24

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2015-August
ISSN (Print)1520-6149

Other

Other40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015
CountryAustralia
CityBrisbane
Period14-04-1914-04-24

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All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

Cite this

Huang, K. Y., Lin, J. K., Chiu, Y. H., & Wu, C. H. (2015). Affective structure modeling of speech using probabilistic context free grammar for emotion recognition. In 2015 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015 - Proceedings (pp. 5286-5290). [7178980] (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2015-August). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP.2015.7178980