Acoustic modeling for emotion recognition
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 resul...
| Main Author: | Anne, Koteswara Rao, |
|---|---|
| Other Authors: | Kuchibhotla, Swarna,, Vankayalapati, Hima Deepthi,, SpringerLink (Online service) |
| Format: | eBook |
| Language: | English |
| Published: |
Cham :
Springer,
[2015]
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| Physical Description: |
1 online resource (vii, 66 pages). |
| Series: |
SpringerBriefs in electrical and computer engineering. Speech technology.
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| Subjects: |
| Summary: |
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 Description: |
Includes bibliographical references. 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. |
| Physical Description: |
1 online resource (vii, 66 pages). |
| Bibliography: |
Includes bibliographical references. |
| ISBN: |
9783319155302 331915530X 3319155296 9783319155296 |