Speech Emotion Recognition Based on Attention MCNN Combined With Gender Information

Emotion recognition is susceptible to interference such as feature redundancy and speaker gender differences, resulting in low recognition accuracy. This paper proposes a speech emotion recognition (SER) method based on attention mixed convolutional neural network (MCNN) combined with gender information, including two stages of gender recognition and emotion recognition. (1) Using MCNN to identify gender and classify speech samples into male and female. (2) According to the output of the first stage classification, a gender-specific emotion recognition model is established by introducing coordinated attention and a series of gated recurrent network units connecting the attention mechanism (A-GRUs) to achieve emotion recognition results of different genders. The inputs of both stages are dynamic 3D MFCC features generated from the original speech database. The proposed method achieves 95.02% and 86.34% accuracy on EMO-DB and RAVDESS datasets, respectively. The experimental results show that the proposed SER system combined with gender information significantly improves the recognition performance.