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Speaker independent emotion recognition using HMMs fusion system with relative features

  • Liqin Fu*
  • , Xia Mao
  • , Lijiang Chen
  • *Corresponding author for this work
  • Beihang University

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Speaker Independent Emotion Recognition is particularly difficult for the individual differences of acoustic character and culture background. So, relative features obtained by calculating the features change of emotion speech relative to natural speech are adopted to weaken the influence from the individual differences in the paper. Moreover, an improved ranked voting fusion system is proposed to combine the decisions from four hidden Markov model (HMM) classifiers which are based on different feature vectors respectively. The recognition results of the provided algorithm have been compared with the isolated HMMs with absolute features, by Berlin database of emotional speech, and the average recognition rate has reached 78.4% in speaker independent case.

Original languageEnglish
Title of host publicationProceedings - The 1st International Conference on Intelligent Networks and Intelligent Systems, ICINIS 2008
Pages608-611
Number of pages4
DOIs
StatePublished - 2008
Event1st International Conference on Intelligent Networks and Intelligent Systems, ICINIS 2008 - Wuhan, China
Duration: 1 Nov 20083 Nov 2008

Publication series

NameProceedings - The 1st International Conference on Intelligent Networks and Intelligent Systems, ICINIS 2008

Conference

Conference1st International Conference on Intelligent Networks and Intelligent Systems, ICINIS 2008
Country/TerritoryChina
CityWuhan
Period1/11/083/11/08

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