Central Library

Normal view MARC view ISBD view

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

By: Rao, K. Sreenivasa [author.].
Contributor(s): Koolagudi, Shashidhar G [author.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: SpringerBriefs in Electrical and Computer Engineering: Publisher: New York, NY : Springer New York : Imprint: Springer, 2013Description: XII, 118 p. 37 illus., 15 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9781461463603.Subject(s): Engineering | User interfaces (Computer systems) | Computational linguistics | Engineering | Signal, Image and Speech Processing | User Interfaces and Human Computer Interaction | Language Translation and Linguistics | Computational LinguisticsAdditional physical formats: Printed edition:: No titleDDC classification: 621.382 Online resources: Click here to access online
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.
    average rating: 0.0 (0 votes)
No physical items for this record

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.

There are no comments for this item.

Log in to your account to post a comment.