Speech Emotion Recognition Using Convolutional Neural Networks with Attention Mechanism

Speech emotion recognition (SER) is an interesting and difficult problem to handle. In this paper, we deal with it through the implementation of deep learning networks. We have designed and implemented six different deep learning networks, a deep belief network (DBN), a simple deep neural network (SDNN), an LSTM network (LSTM), an LSTM network with the addition of an attention mechanism (LSTM-ATN), a convolutional neural network (CNN), and a convolutional neural network with the addition of an attention mechanism (CNN-ATN), having in mind, apart from solving the SER problem, to test the impact of the attention mechanism on the results. Dropout and batch normalization techniques are also used to improve the generalization ability (prevention of overfitting) of the models as well as to speed up the training process. The Surrey Audio–Visual Expressed Emotion (SAVEE) database and the Ryerson Audio–Visual Database (RAVDESS) were used for the training and evaluation of our models. The results showed that the networks with the addition of the attention mechanism did better than the others. Furthermore, they showed that the CNN-ATN was the best among the tested networks, achieving an accuracy of 74% for the SAVEE database and 77% for the RAVDESS, and exceeding existing state-of-the-art systems for the same datasets.