Emotion Recognition using Speech Features, 2013
SpringerBriefs in Speech Technology Series

?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.

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)

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

Discusses complete state-of -art features, models and databases in the context of emotion recognition Explores implicit and explicit excitation source features for discriminating the emotions Proposes pitch synchronous and sub-syllabic spectral features, in addition to conventional spectral features, for characterizing emotions Includes supplementary material: sn.pub/extras