Titlebook: Emotion Recognition using Speech Features; K. Sreenivasa Rao,Shashidhar G. Koolagudi Book 2013 Springer Science+Business Media New York 20

卷發(fā)

Extracellular Matrix and Cardiac Remodelingfferent vocal tract features are compared over Indian and Berlin emotional speech databases. Performance of neural networks and Gaussian mixture models in classifying the emotional utterances based on vocal tract features is also evaluated.

憤慨一下

Emotion Recognition Using Vocal Tract Information,fferent vocal tract features are compared over Indian and Berlin emotional speech databases. Performance of neural networks and Gaussian mixture models in classifying the emotional utterances based on vocal tract features is also evaluated.

SIT

可用

B. Stea,D. Shimm,J. Kittelson,T. Cetasines are used for capturing the emotion-specific information from the proposed global and local prosodic features. Performance of the developed emotion recognition systems are analyzed with respect to individual components of prosody and their combinations.

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Stable-Angina

MIR

執(zhí)拗

Speech Emotion Recognition: A Review, of emotional databases such as simulated, elicited and natural are critically reviewed from the research point of view. Review of existing emotion recognition systems developed using excitation source, vocal tract system and prosodic features is briefly presented. Basic pattern classification model

CHYME

六邊形

Emotion Recognition Using Vocal Tract Information,pstral coefficients (LPCCs) and mel frequency cepstral coefficients (MFCCs) are used as the correlates of vocal tract information for discriminating the emotions. In addition to LPCCs and MFCCs, formant related features are also explored in this work for recognizing emotions from speech. Extraction