Spatiotemporal and frequential cascaded attention networks for speech emotion recognition

Speech emotion recognition is an important but difficult task in human–computer interaction systems. One of the main challenges in speech emotion recognition is how to extract effective emotion features from a long utterance. To address this issue, we propose a novel spatiotemporal and frequential cascaded attention network with large-margin learning in this paper. Spatiotemporal attention selectively locates the targeted emotional regions from a long speech spectrogram. In these targeted regions, frequential attention captures the emotional features by frequency distribution. The cascaded attention assists the neural network to gradually extract effective emotion features from the long spectrogram. During training, large-margin learning is applied to improve intra-class compactness and enlarge inter-class distances. Experiments on four public datasets demonstrate that our proposed model achieves a promising performance in speech emotion recognition.