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Acoustic Modeling for Emotion Recognition [electronic resource] / by Koteswara Rao Anne, Swarna Kuchibhotla, Hima Deepthi Vankayalapati.

By: Contributor(s): Series: SpringerBriefs in Electrical and Computer EngineeringPublisher: Cham : Springer International Publishing : Imprint: Springer, 2015Description: VII, 66 p. 24 illus., 17 illus. in color. online resourceContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9783319155302
Subject(s): Genre/Form: Additional physical formats: Printed edition:: No titleDDC classification:
  • 621.382 23
LOC classification:
  • TK5102.9
  • TA1637-1638
  • TK7882.S65
Online resources:
Contents:
Introduction -- Emotion Recognition using Prosodic features -- Emotion Recognition using Spectral features -- Emotional Speech Corpora -- Classification Models -- Comparative Analysis of Classifiers in emotion recognition -- Summary and Conclusions.
In: Springer eBooksSummary:  This book presents state of art research in speech emotion recognition. Readers are first presented with basic research and applications – gradually more advance information is provided, giving readers comprehensive guidance for classify emotions through speech. Simulated databases are used and results extensively compared, with the features and the algorithms implemented using MATLAB. Various emotion recognition models like Linear Discriminant Analysis (LDA), Regularized Discriminant Analysis (RDA), Support Vector Machines (SVM) and K-Nearest neighbor (KNN) and are explored in detail using prosody and spectral features, and feature fusion techniques.
Item type: eBooks
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Introduction -- Emotion Recognition using Prosodic features -- Emotion Recognition using Spectral features -- Emotional Speech Corpora -- Classification Models -- Comparative Analysis of Classifiers in emotion recognition -- Summary and Conclusions.

 This book presents state of art research in speech emotion recognition. Readers are first presented with basic research and applications – gradually more advance information is provided, giving readers comprehensive guidance for classify emotions through speech. Simulated databases are used and results extensively compared, with the features and the algorithms implemented using MATLAB. Various emotion recognition models like Linear Discriminant Analysis (LDA), Regularized Discriminant Analysis (RDA), Support Vector Machines (SVM) and K-Nearest neighbor (KNN) and are explored in detail using prosody and spectral features, and feature fusion techniques.

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