K. Sreenivasa Rao, Shashidhar G. Koolagudi

Robust Emotion Recognition using Spectral and Prosodic Features

• Broschiertes Buch

Produktbeschreibung

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.

Produktdetails

• SpringerBriefs in Speech Technology
• Verlag: Springer / Springer New York / Springer, Berlin
• Artikelnr. des Verlages: 978-1-4614-6359-7
• 2013
• Seitenzahl: 132
• Erscheinungstermin: 12. Januar 2013
• Englisch
• Abmessung: 235mm x 155mm x 8mm
• Gewicht: 221g
• ISBN-13: 9781461463597
• ISBN-10: 1461463599
• Artikelnr.: 36831811

Autorenporträt

K. Sreenivasa Rao is at Indian Institute of Technology, Kharagpur, India. Shashidhar G, Koolagudi is at Graphic Era University, Dehradun, India.

Inhaltsangabe

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).

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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).