关键词: 语谱图, 深度学习, 参数迁移, 卷积循环神经网络, 语音情感识别

Abstract: In the study of speech emotion recognition, most methods based on deep learning don’t model the time-frequency characteristics of speech. Moreover, the network model has long training time and the recognition accuracy is not high. The spectrogram is a special image with both time and frequency domains after the conversion of speech signals. In order to fully extract the emotional features of time-frequency domain of the spectrogram, this paper proposes a speech emotion recognition model based on parameter transfer and convolutional recurrent neural network. The proposed model uses the spectrogram as the input of network, introduces the AlexNet network model, and transfers its weighting parameters of pre-trained convolutional layer. The output feature maps of convolutional neural network is put into long short-term memory neural networks for training after being reconstructed. The experimental results show that the proposed method has faster speed of network training and higher accuracy of emotion recognition.

Key words: spectrogram, deep learning, parameter transfer, convolutional recurrent neural network, speech emotion recognition