Speech Emotion Recognition Using a Dual-Channel Complementary Spectrogram and the CNN-SSAE Neutral Network
Juan Li; Xueying Zhang; Lixia Huang; Fenglian Li; Shufei Duan; Ying Sun
In the background of artificial intelligence, the realization of smooth communication between people and machines has become the goal pursued by people. Mel spectrograms is a common method used in speech emotion recognition, focusing on the low-frequency part of speech. In contrast, the inverse Mel (IMel) spectrogram, which focuses on the high-frequency part, is proposed to comprehensively analyze emotions. Because the convolutional neural network-stacked sparse autoencoder (CNN-SSAE) can extract deep optimized features, the Mel-IMel dual-channel complementary structure is proposed. In the first channel, a CNN is used to extract the low-frequency information of the Mel spectrogram. The other channel extracts the high-frequency information of the IMel spectrogram. This information is transmitted into an SSAE to reduce the number of dimensions, and obtain the optimized information. Experimental results show that the highest recognition rates achieved on the EMO-DB, SAVEE, and RAVDESS datasets were 94.79%, 88.96%, and 83.18%, respectively. The conclusions are that the recognition rate of the two spectrograms was higher than that of each of the single spectrograms, which proves that the two spectrograms are complementary. The SSAE followed the CNN to get the optimized information, and the recognition rate was further improved, which proves the effectiveness of the CNN-SSAE network.
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