Research on Speech Emotion Recognition Method Based on ResSE_CNN1D

This article examines a speech emotion recognition (SER) technique based on the enhanced one-dimensional convolution neural network ResSE_CNN1D. With the artificial intelligence developing rapidly, SER has a profound impact in many areas. The model of this article is used to extract the characteristics of the input data through opensmile, and is sent into the ResSE_CNN1D model, which is eventually classified by the softmax activation function and obtains the final results. The key to this model is efficient learning of decimal sets and the rapid deployment in resource-constrained environments. The ResSE_CNN1D model improves the performance of the model by adding the residual connection and the SE module on the basis of cnn1d. This increasing the accuracy of the recognition and preventing the fitting problem. After the model was created, the study adopted the audio sampling and training of the casia data concentration. The final accuracy was 0.900, which increased the accuracy of 2.9 percent compared to the cnn1d method. And by the analysis of the relationship diagram of the confusion matrix and the accuracy and loss rate relative to the number of training, the model has a high robustness and effectively prevents the appearance of the fitting problem. And it also can achieve high precision and achieve a lightweight goal relative to less training.