Abstract: Aiming at the problem that there are many irrelevant features and low accuracy in the existing speech emotion recognition, a speech emotion recognition method based on mixed distributed attention mechanism and hybrid neural network is proposed. The method  is in two channels, and the convolutional neural network and bidirectional short and long-time memory network are used to extract the spatial and temporal features of speech respectively, Then, the outputs of the two networks are used as the input matrix of the multi-head attention mechanism. At the same time, considering the low-rank distribution problem of the existing multi-head attention mechanism, the attention mechanism calculation method is improved. The low rank distribution and the similarity of the output characteristics of the two neural networks are superimposed by mixed distribution. After the normalization operation, all the subspace results are stitched together. Finally, the output is classified through the full connection layer. The experimental results show that, the speech emotion recognition method based on mixed distributed attention mechanism and hybrid neural network has higher accuracy than other existing models, verify- ing the validity of the proposed method.

Key words: speech emotion recognition, Mel frequency cepstral coefficient, bidirectional long short-term memory network, convolutional neural network, multi-head attention mechanism