K. Sreenivasa Rao, Shashidhar G. Koolagudi

Emotion Recognition using Speech Features (eBook, PDF)

• Format: PDF

Produktbeschreibung

"Emotion Recognition Using Speech Features" provides coverage of emotion-specific features present in speech. The author also discusses suitable models for capturing emotion-specific information for distinguishing different emotions. The content of this book is important for designing and developing natural and sophisticated speech systems. In this Brief, Drs. Rao and Koolagudi lead a discussion of how emotion-specific information is embedded in speech and how to acquire emotion-specific knowledge using appropriate statistical models. Additionally, the authors provide information about exploiting multiple evidences derived from various features and models. The acquired emotion-specific knowledge is useful for synthesizing emotions. Features includes discussion of: . Global and local prosodic features at syllable, word and phrase levels, helpful for capturing emotion-discriminative information; . Exploiting complementary evidences obtained from excitation sources, vocal tract systems and prosodic features in order to enhance the emotion recognition performance; . Proposed multi-stage and hybrid models for improving the emotion recognition performance. This brief is for researchers working in areas related to speech-based products such as mobile phone manufacturing companies, automobile companies, and entertainment products as well as researchers involved in basic and applied speech processing research.

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Produktdetails

• Verlag: Springer US
• Seitenzahl: 124
• Erscheinungstermin: 7. November 2012
• Englisch
• ISBN-13: 9781461451433
• Artikelnr.: 43790480

Autorenporträt

K. Sreenivasa Rao is at the Indian Institute of Technology, Kharagpur, India. Shashidhar G, Koolagudi is at the Graphic Era University, Dehradun, India.

Inhaltsangabe

Introduction.- Speech Emotion Recognition: A Review.- Emotion Recognition Using Excitation Source Information.- Emotion Recognition Using Vocal Tract Information.- Emotion Recognition Using Prosodic Information.- Summary and Conclusions.- Linear Prediction Analysis of Speech.- MFCC Features.- Gaussian Mixture Model (GMM)