Robust Emotion Recognition using Spectral and Prosodic Features (SpringerBriefs in Speech Technology)

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. Front Matter....Pages i-xii Introduction....Pages 1-15 Robust Emotion Recognition using Pitch Synchronous and Sub-syllabic Spectral Features....Pages 17-46 Robust Emotion Recognition using Sentence, Word and Syllable Level Prosodic Features....Pages 47-69 Robust Emotion Recognition using Combination of Excitation Source, Spectral and Prosodic Features....Pages 71-84 Robust Emotion Recognition using Speaking Rate Features....Pages 85-94 Emotion Recognition on Real Life Emotions....Pages 95-100 Summary and Conclusions....Pages 101-107 Back Matter....Pages 109-118 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).