Hierarchical modeling of temporal course in emotional expression for speech emotion recognition

Chung Hsien Wu, Wei Bin Liang, Kuan Chun Cheng, Jen Chun Lin

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

4 Citations (Scopus)

Abstract

This paper presents an approach to hierarchical modeling of temporal course in emotional expression for speech emotion recognition. In the proposed approach, a segmentation algorithm is employed to hierarchically chunk an input utterance into three-level temporal units, including low-level descriptors (LLDs)-based sub-utterance level, emotion profile (EP)-based sub-utterance level and utterance level. An emotion-oriented hierarchical structure is constructed based on the three-level units to describe the temporal emotion expression in an utterance. A hierarchical correlation model is also proposed to fuse the three-level outputs from the corresponding emotion recognizers and further model the correlation among them to determine the emotional state of the utterance. The EMO-DB corpus was used to evaluate the performance on speech emotion recognition. Experimental results show that the proposed method considering the temporal course in emotional expression provides the potential to improve the speech emotion recognition performance.

Original languageEnglish
Title of host publication2015 International Conference on Affective Computing and Intelligent Interaction, ACII 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages810-814
Number of pages5
ISBN (Electronic)9781479999538
DOIs
Publication statusPublished - 2015 Dec 2
Event2015 International Conference on Affective Computing and Intelligent Interaction, ACII 2015 - Xi'an, China
Duration: 2015 Sep 212015 Sep 24

Publication series

Name2015 International Conference on Affective Computing and Intelligent Interaction, ACII 2015

Other

Other2015 International Conference on Affective Computing and Intelligent Interaction, ACII 2015
CountryChina
CityXi'an
Period15-09-2115-09-24

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Human-Computer Interaction
  • Software

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