Cubic SVM Classifier Based Feature Extraction and Emotion Detection from Speech Signals

The detection of emotions from the speech is one of the most stirring and intriguing research areas in the field of artificial intelligence. In this paper, the emotion identification from Hindi language speech which is a popular language of India is carried out in a noisy environment after which multifarious emotions are classified into 4 main groups of emotional states namely happiness, sadness, anger and neutral. The proposed technique involves extraction of prosodic and spectral features of an acoustic signal like pitch, energy, formant, Mel-frequency Cepstrum Coefficients (MFCC) and Linear Prediction Cepstral Coefficient (LPCC) along with their classification using a cubic spine Support Vector Machine (SVM) classifier model. The system gave an overall accuracy of, 98.75% in male actor utterances and 95% in female actors. Experimental results manifest that the proposed technique garners better accuracy by correctly identifying the emotions and these results were moreover compared to the other existing methods of speech emotion detection. Furthermore, the extracted features along with, different classifier models were contrasted in this paper for better evaluation.