Emotion recognition using speech features / / Sreenivasa Rao Krothapalli, Shashidhar G. Koolagudi

• Record Nr.: UNINA9910437893803321
• Autore: Krothapalli Sreenivasa Rao
• Pubbl/distr/stampa: New York, : Springer, 2013
• ISBN: 9781283908764; 128390876X; 9781461451433; 1461451434
• Edizione: [1st ed. 2013.]
• Descrizione fisica: 1 online resource (133 p.)
• Collana: SpringerBriefs in electrical and computer engineering : SpringerBriefs in speech technology, , 2191-8112
• Altri autori (Persone): KoolagudiShashidhar G
• Disciplina: 006.454
• Soggetti: Emotions; Speech perception
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Description based upon print version of record.
• Nota di bibliografia: Includes bibliographical references.
• Nota di contenuto: 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).
• Sommario/riassunto: “Emotion Recognition Using Speech Features” covers emotion-specific features present in speech and discussion of 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. 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 using evidence derived from various features and models. The acquired emotion-specific knowledge is useful for synthesizing emotions. Discussion includes global and local prosodic features at syllable, word and phrase levels, helpful for capturing ; emotion-discriminative information; use of complementary evidences obtained from excitation sources, vocal tract systems and prosodic features in order to enhance the emotion recognition performance; and proposed multi-stage and hybrid models for improving the emotion recognition performance.