Paper Info
Title
Affective Structure Modeling Of Speech Using Probabilistic Context Free Grammar For Emotion Recognition
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.
Year
Venue
Keywords
2015
2015 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING (ICASSP)
Speech emotion recognition, probabilistic context free grammar, affective structure model
Field
DocType
ISSN
Context-free grammar,Computer science,Support vector machine,Utterance,Speech recognition,Emotional expression,Vector quantization,Natural language processing,Artificial intelligence,Affective computing,Probabilistic logic,Hidden Markov model
Conference
1520-6149
Citations 
PageRank 
References 
1
0.39
13
Authors
4
Name
Order
Citations
PageRank
Kun-Yi Huang1145.00
Jia-Kuan Lin210.39
Yu-Hsien Chiu310.39
Chung-Hsien Wu41099116.79