Long-Time Speech Emotion Recognition Using Feature Compensation and Accentuation-Based Fusion

In this paper, we study the speech emotion feature optimization using stochastic optimization algorithms, and feature compensation using deep neural networks. We also proposed to use accentuation-based fusion for long-time speech emotion recognition. Firstly, the extraction method of emotional features is studied, and a series of speech features are constructed for the recognition of emotion. Secondly, we propose a method of sample adaptation through denoising autoencoder to enhance the versatility of features through the mapping of sample features to improve adaptive ability. Thirdly, GA and SFLA are used to optimize the combination of features to improve the emotion recognition results at the utterance level. Finally, we use transformer model to implement accentuation-based emotion fusion in long-time speech. The continuous long-time speech corpus, as well as the public available EMO-DB, are used for experiments. Results show that the proposed method can effectively improve the performance of long-time speech emotion recognition.