Abstract: In this paper, we studied the algorithm of speech emotion recognition based on convolutional neural networks, and improved the algorithm of updating convolution kernel weight during the training process of traditional convolutional neural networks, resulting that the algorithm of updating the convolution kernel weight was related to the number of iterations. Simultaneously, in order to increase the difference of emotional phonetic features, the data matrix of the Mel-frequency cepstral coefficients (MFCC) obtained by preprocessing the speech signal was transformed, consequently, improved the expressive ability of convolutional neural networks. Experiments showed that the error recognition rate of the improved algorithm of speech emotion recognition was about 7% lower than that of traditional algorithms.

Key words: speech emotion recognition, convolutional neural networks, Mel-frequency cepstral coefficients (MFCC), recognition rate