
Robust Emotion Recognition using Spectral and Prosodic Features
Springer (Publisher)
Published on 12. January 2013
Book
Paperback/Softback
XII, 118 pages
978-1-4614-6359-7 (ISBN)
Description
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.
More details
Series
Edition
2013 ed.
Language
English
Place of publication
New York
United States
Target group
Professional and scholarly
Research
Illustrations
22 s/w Abbildungen, 15 farbige Abbildungen
XII, 118 p. 37 illus., 15 illus. in color.
Dimensions
Height: 235 mm
Width: 155 mm
Thickness: 8 mm
Weight
213 gr
ISBN-13
978-1-4614-6359-7 (9781461463597)
DOI
10.1007/978-1-4614-6360-3
Schweitzer Classification
Other editions
Additional editions

K. Sreenivasa Rao | Shashidhar G. Koolagudi
Robust Emotion Recognition using Spectral and Prosodic Features
E-Book
01/2013
1st Edition
Springer
€53.49
Available for download
Persons
K. Sreenivasa Rao is at Indian Institute of Technology, Kharagpur, India.
Shashidhar G, Koolagudi is at Graphic Era University, Dehradun, India.
Content
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).