The Impact of Attention Mechanisms on Speech Emotion Recognition

Shouyan Chen, Mingyan Zhang, Xiaofen Yang, Zhijia Zhao, Tao Zou, Xinqi Sun
2021 Sensors  
Speech emotion recognition (SER) plays an important role in real-time applications of human-machine interaction. The Attention Mechanism is widely used to improve the performance of SER. However, the applicable rules of attention mechanism are not deeply discussed. This paper discussed the difference between Global-Attention and Self-Attention and explored their applicable rules to SER classification construction. The experimental results show that the Global-Attention can improve the accuracy
more » ... f the sequential model, while the Self-Attention can improve the accuracy of the parallel model when conducting the model with the CNN and the LSTM. With this knowledge, a classifier (CNN-LSTM×2+Global-Attention model) for SER is proposed. The experiments result show that it could achieve an accuracy of 85.427% on the EMO-DB dataset.
doi:10.3390/s21227530 pmid:34833603 pmcid:PMC8622179 fatcat:r6njkft5hrdn3evu5gev462vzm