Emotion recognition using IG-based feature compensation and continuous support vector machines

Chung Hsien Wu, Ze Jing Chuang

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

Abstract

This paper presents an approach to feature compensation for emotion recognition from speech signals. In this approach, the intonation groups (IGs) of the input speech signals are firstly extracted. The speech features in each selected intonation group are then extracted. With the assumption of linear mapping between feature spaces in different emotional states, a feature compensation approach is proposed to characterize the feature space with better discriminability among emotional states. The compensation vector with respect to each emotional state is estimated using the Minimum Classification Error (MCE) algorithm. For the final emotional state decision, the IG-based feature vectors compensated by the compensation vectors are used to train the Continuous Support Vector Machine (CSVMs) for each emotional state. The emotional state with the maximal output probability is determined as the final output. The kernel function of CSVM model is experimentally decided as Radial basis function and the experimental result shows that IG-based feature extraction and compensation can obtain encouraging performance for emotion recognition.

Original languageEnglish
Title of host publication3rd International Conference on Speech Prosody 2006
EditorsR. Hoffmann, H. Mixdorff
PublisherInternational Speech Communications Association
ISBN (Electronic)9780000000002
Publication statusPublished - 2006
Event3rd International Conference on Speech Prosody, SP 2006 - Dresden, Germany
Duration: 2006 May 22006 May 5

Publication series

NameProceedings of the International Conference on Speech Prosody
ISSN (Print)2333-2042

Conference

Conference3rd International Conference on Speech Prosody, SP 2006
CountryGermany
CityDresden
Period06-05-0206-05-05

All Science Journal Classification (ASJC) codes

  • Language and Linguistics
  • Linguistics and Language

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