Emotion recognition from speech has emerged as an important research area in the recent past. The purpose of speech emotion recognition system is to automatically classify speaker's utterances into seven emotional states including anger, boredom, disgust, fear, happiness, sadness and neutral. The speech samples are from Berlin emotional database and the features extracted from these utterances are Teager-based delta-spectral cepstral coefficients (T-DSCC) which are shown to perform better than MFCC. Dynamic Time Warping (DTW) and its variant Improved Features for DTW (IFDTW) is used as a classifier to classify different emotional states. Unlike in conventional DTW, we do not use the minimum distance for classification. Rather, the median distance criterion is employed for improved emotion classification. The proposed emotion recognition system gives an overall classification accuracy of 97. 52%.

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Emotion Recognition Mfcc Dscc Teager Energy Operator Dynamic Time Warping