Emotion Recognition using Speech Features [electronic resource] / by K. Sreenivasa Rao, Shashidhar G. Koolagudi.Material type: TextLanguage: English Series: SpringerBriefs in Electrical and Computer Engineering, SpringerBriefs in Speech Technology: Publisher: New York, NY : Springer New York : Imprint: Springer, 2013Description: XII, 124 p. 30 illus., 6 illus. in color. online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9781461451433Subject(s): Engineering | Computer science | Computational linguistics | Engineering | Signal, Image and Speech Processing | User Interfaces and Human Computer Interaction | Computational LinguisticsAdditional physical formats: Printed edition:: No titleDDC classification: 621.382 LOC classification: TK5102.9TA1637-1638TK7882.S65Online resources: Click here to access online
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).
“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.