Browsing by Author "Kokane Amol, T."

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    Multiclass SVM-based language-independent emotion recognition using selective speech features
    (Institute of Electrical and Electronics Engineers Inc., 2014) Kokane Amol, T.; Guddeti, G.R.M.
    In this paper, we emphasize on recognizing six basic emotions viz. Anger, Disgust, Fear, Happiness, Neutral and Sadness using selective features of speech signal of different languages like Germen and Telugu. The feature set includes thirteen Mel-Frequency Cepstral Coefficients (MFCC) and four other features of speech signal such as Energy, Short Term Energy, Spectral Roll-Off and Zero-Crossing Rate (ZCR). The Surrey Audio-Visual Expressed Emotion (SAVEE) Database is used to train the Multiclass Support Vector Machine (SVM) classifier and a German Corpus EMO-DB (Berlin Database of Emotional Speech) and Telugu Corpus IITKGP: SESC are used for emotion recognition. The results are analyzed for each speech emotion separately and obtained accuracies of 98.3071% and 95.8166 % for Emo-DB, IITKGP: SESC databases respectively. © 2014 IEEE.