Impact of Supervised Classifier on Speech Emotion Recognition

Abstract

A face recognition system is a computer application proficient of verifying or identifying a person from a video frame or a digital image from a video source. The human face acts a significant role in the social communication, passing on people’s uniqueness. By means of the human face as a key to protection, biometric face recognition technology has attained noteworthy consideration in the precedent numerous years owing to its prospective for an extensive assortment of applications in both non-law enforcement and law enforcement activities. In this paper, the Speech Emotion Recognition (SER) is analyzed by adopting cepstral features for feature extraction and k-NN classifier for classification. Moreover, the implemented process is compared with k-means and C-means algorithms and the results are obtained.