Speech emotion recognition algorithm based on spectrogram feature extraction of deep space attention feature - Jinhua WANG; Na YING; Chendu ZHU; Zhaosen LIU; Zhedong CAI (Dianxin kexue)
Starts from the extraction and classification modeling of speech emotion features, based on the hybrid convolutional neural network model, the Itti model in feature extraction was improved, including increasing the extraction by local binary mode. The strong correlation features were extracted combining with the sensitivity of the auditory sensitivity. Then, the constrained extrusion and excitation network structure of the calibration weights were extracted by feature constraints. Finally, a fine-tuning model based on VGGnet and long-short-time memory network hybrid network was formed, further enhancing the ability to express emotions. By validating on the natural sentiment database and the German-German database, the model had a significant increase in the rate of sentiment recognition, which is 8. 43% higher than the benchmark model. At the same time, the recognition effect of the model on the natural database (FAU-AEC) and the Berlin database (EMO-DB) were compared. The experimental results show that the model has a good generalization.