Emotion recognition in speech using inter-sentence glottal statistics

Alexander I. Iliev, Michael S. Scordilis

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

7 Scopus citations

Abstract

This study deals with the recognition of three emotional states in speech, namely: Happiness, Anger, and Sadness. The corpus included speech from six subjects (3M and 3F) speaking ten sentences. Glottal inverse filtering was first performed on the spoken utterances. Then parameters for computing the Glottal Symmetry were collected and computed to create a final matrix of features. A combined with all emotions across the different subjects was formed and used to train a Gaussian Mixture Model (GMM) classifier. Training on 80% of all combined utterances for each emotion was performed. Testing was administered on the remaining 20%. The system shows confidence that glottal information may be used for determining the correct emotion in speech. The recognition performance varied between 48.96% and 82.29%.

Original languageEnglish (US)
Title of host publicationProceedings of IWSSIP 2008 - 15th International Conference on Systems, Signals and Image Processing
Pages465-468
Number of pages4
DOIs
StatePublished - Oct 6 2008
Event15th International Conference on Systems, Signals and Image Processing, IWSSIP 2008 - Bratislava, Slovakia
Duration: Jun 25 2008Jun 28 2008

Publication series

NameProceedings of IWSSIP 2008 - 15th International Conference on Systems, Signals and Image Processing

Other

Other15th International Conference on Systems, Signals and Image Processing, IWSSIP 2008
CountrySlovakia
CityBratislava
Period6/25/086/28/08

Keywords

  • Emotion recognition
  • Glottal symmetry
  • Glottal waveform
  • GMM
  • Pattern classification
  • Speech

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Control and Systems Engineering

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