Robust Emotion Recognition using Spectral and Prosodic Features
By: by K. Sreenivasa Rao, Shashidhar G. Koolagudi
Contributors: Koolagudi, Shashidhar G.; SpringerLink (Online service)
Series: SpringerBriefs in Electrical and Computer Engineering
Publisher: Springer New York; Imprint: Springer
Published: 2013
Place of publication: New York, NY
Language: eng
Format: [electronic resource] /
Number of pages: 118
ISBN-13: 9781461463603
Subjects: Engineering; Translators (Computer programs); Computer science; Language Translation and Linguistics; Image and Speech Processing Signal; User Interfaces and Human Computer Interaction; Computational linguistics
Description: <p>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.</p><p>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.</p>
Table of contents: title: Introduction; title: Robust Emotion Recognition using Pitch Synchronous and Sub-syllabic Spectral Features; title: Robust Emotion Recognition using Word and Syllable Level Prosodic Features; title: Robust Emotion Recognition using Combination of Excitation Source, Spectral and Prosodic Features; title: Robust Emotion Recognition using Speaking Rate Features; title: Emotion Recognition on Real Life Emotions; title: Summary and Conclusions; title: MFCC Features; title: Gaussian Mixture Model (GMM).