Convolution neural network based automatic speech emotion recognition using Mel-frequency Cepstrum coefficients

A significant role is played by Speech Emotion Recognition (SER) with different applications in affective computing and human-computer interface. In literature, the most adapted technique for recognition of emotion was based on simple feature extraction using a simple classifier. Most of the methods in the literature has limited efficiency for the recognition of emotion. Hence for solving these drawbacks, five various models based on Convolution Neural Network (CNN) was proposed in this paper for recognition of emotion through signals obtained on speech. In the methodology which was proposed, seven different emotions are recognised with the utilisation of CNN with feature extraction methods includes disgust, normal, fear Joy, Anger, Sadness and surprise. Initially, the speech emotion signals are collected from the database such as berlin database. After that, feature extraction is considered, and it is carried out by the Pitch and Energy, Mel-Frequency Cepstral Coefficients (MFCC) and Mel Energy Spectrum Dynamic Coefficients (MEDC). The mentioned feature extraction process is widely used for classifying the speech data and perform better in performance. Mel-cepstral coefficients utilise less time for shaping the spectral with adequate data and offers better voice quality. The extracted features are used for the recognition purpose by CNN network. In the proposed CNN network, either one or more pairs of convolutions, besides, max-pooling layers remain present. With the utilisation of the CNN network, the emotions are recognised through the input speech signal. The proposed method is implemented in MATLAB, and it will be contrasted with the existing method such as Linear Prediction Cepstral Coefficient (LPCC) with the K-Nearest Neighbour (KNN) classifier to test the samples for optimal performance evaluation. The Statistical measurements are utilised for analysing the performance such as accuracy, precision, specificity, recall, sensitivity, error rate, receiver operating characteristics (ROC) curve, an area under curve (AUC), and False Positive Rate (FPR).