Audiovisual emotion recognition based on bi-layer LSTM and multi-head attention mechanism on RAVDESS dataset

Recognizing and classifying emotions is important. It is key in medicine, transportation, and artificial intelligence. This paper aims to address the problem. The problem is poor emotion classification due to a bad fusion of many modes. It proposes an audio-visual emotion recognition method. It is based on bilayer LSTM and multi-head attention on the RAVDESS dataset. It considers the fusion of voice and facial expression features. The network learns MFCC and facial features. It uses a convolutional layer for MFCC features and a double-layer LSTM for facial features. Then, it fuses the features of both modalities using a multi-head attention module. Finally, it convolves, pools, and splices the learned features for emotion recognition. In our experiments, the accuracy on the public dataset RAVDESS is 82.42%.We got this result using 5-fold cross-validation. Comparison with other methods shows that the method improves audio-visual emotion recognition.