Speech Emotion Recognition via CNN-Transformer and multidimensional attention mechanism

Speech Emotion Recognition (SER) is crucial in human-machine interactions. Previous approaches have predominantly focused on local spatial or channel information and neglected the temporal information in speech. In this paper, to model local and global information at different levels of granularity in speech and capture temporal, spatial and channel dependencies in speech signals, we propose a Speech Emotion Recognition network based on CNN-Transformer and multi-dimensional attention mechanisms. Specifically, a stack of CNN blocks is dedicated to capturing local information in speech from a time-frequency perspective. In addition, a time-channel-space attention mechanism is used to enhance features across three dimensions. Moreover, we model local and global dependencies of feature sequences using large convolutional kernels with depthwise separable convolutions and lightweight Transformer modules. We evaluate the proposed method on IEMOCAP and Emo-DB datasets and show our approach significantly improves the performance over the state-of-the-art methods. https://github.com/SCNU-RISLAB/CNN-Transforemr-and-MultidimensionalAttention-Mechanism.