Study of Speech Emotion Recognition Using Blstm with Attention

We present a study of a neural network-based method for speech emotion recognition that uses audio-only features. In the studied scheme, the acoustic features are extracted from the audio utterances and fed to a neural network that consists of convolutional neural networks (CNN) layers, bidirectional long short-term memory (BLSTM) combined with an attention mechanism layer, and a fully-connected layer. To illustrate and analyze the classification capabilities of the network, we used the t-distributed stochastic neighbor embedding (t-SNE) method. We evaluate our model using Ryerson audio-visual dataset of emotional speech and song (RAVDESS) and interactive emotional dyadic motion capture (IEMOCAP) datasets achieving weighted accuracy (WA) of 80% and 66%, respectively.