Speech Emotion Recognition Based on Convolutional Neural Network for Emergency System of Railway Station

Abstract

This paper proposed a speech emotion recognition model based on Convolutional Neural Network (CNN). The model first extracts the Mel Cepstral Coefficient (MFCC) feature of each speech, and then sends the extracted feature matrix to the convolution the neural network is trained, and finally the category of each voice is output by the network. In addition, a confidence setting is added to the output layer of the model, and it is believed that the probability of each voice belonging to a certain category is greater than 90%. Experimental results show that the model has a higher accuracy rate compared with Recurrent Neural Network (RNN) and Multilayer Perceptron (MLP). This method provides a certain reference for the application of deep learning technology in speech emotion recognition and early warning of dangerous situations in railway stations and other places.