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Robust Emotion Recognition using Spectral and Prosodic Features [electronic resource] / by K. Sreenivasa Rao, Shashidhar G. Koolagudi.

By: Contributor(s): Material type: TextTextSeries: SpringerBriefs in Electrical and Computer EngineeringPublisher: New York, NY : Springer New York : Imprint: Springer, 2013Description: XII, 118 p. 37 illus., 15 illus. in color. online resourceContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9781461463603
Subject(s): Additional physical formats: Printed edition:: No titleDDC classification:
  • 621.382 23
LOC classification:
  • TK5102.9
  • TA1637-1638
  • TK7882.S65
Online resources:
Contents:
Introduction -- Robust Emotion Recognition using Pitch Synchronous and Sub-syllabic Spectral Features -- Robust Emotion Recognition using Word and Syllable Level Prosodic Features -- Robust Emotion Recognition using Combination of Excitation Source, Spectral and Prosodic Features -- Robust Emotion Recognition using Speaking Rate Features -- Emotion Recognition on Real Life Emotions -- Summary and Conclusions -- MFCC Features -- Gaussian Mixture Model (GMM).
In: Springer eBooksSummary: In this brief, the authors discuss recently explored spectral (sub-segmental and pitch synchronous) and prosodic (global and local features at word and syllable levels in different parts of the utterance) features for discerning emotions in a robust manner. The authors also delve into the complementary evidences obtained from excitation source, vocal tract system and prosodic features for the purpose of enhancing emotion recognition performance. Features based on speaking rate characteristics are explored with the help of multi-stage and hybrid models for further improving emotion recognition performance. Proposed spectral and prosodic features are evaluated on real life emotional speech corpus.
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Introduction -- Robust Emotion Recognition using Pitch Synchronous and Sub-syllabic Spectral Features -- Robust Emotion Recognition using Word and Syllable Level Prosodic Features -- Robust Emotion Recognition using Combination of Excitation Source, Spectral and Prosodic Features -- Robust Emotion Recognition using Speaking Rate Features -- Emotion Recognition on Real Life Emotions -- Summary and Conclusions -- MFCC Features -- Gaussian Mixture Model (GMM).

In this brief, the authors discuss recently explored spectral (sub-segmental and pitch synchronous) and prosodic (global and local features at word and syllable levels in different parts of the utterance) features for discerning emotions in a robust manner. The authors also delve into the complementary evidences obtained from excitation source, vocal tract system and prosodic features for the purpose of enhancing emotion recognition performance. Features based on speaking rate characteristics are explored with the help of multi-stage and hybrid models for further improving emotion recognition performance. Proposed spectral and prosodic features are evaluated on real life emotional speech corpus.

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