Fine-Grained Recognition of Fidgety-Type Emotions Using Multi-Scale One-Dimensional Residual Siamese Network

Abstract

Fidgety speech emotion has important research value, and many deep learning models have played a good role in feature modeling in recent years. In this paper, the problem of practical speech emotion is studied, and the improvement is made on fidgety-type emotion using a novel neural network model. First, we construct a large number of phonological features for modeling emotions. Second, the differences in fidgety speech between various groups of people were studied. Through the distribution of features, the individual features of fidgety emotion were studied. Third, we propose a fine-grained emotion classification method, which analyzes the subtle differences between emotional categories through Siamese neural networks. We propose to use multi-scale residual blocks within the network architecture, and alleviate the vanishing gradient problem. This allows the network to learn more meaningful representations of fidgety speech signal. Finally, the experimental results show that the proposed method can provide the versatility of modeling, and that fidgety emotion is well identified. It has great research value in practical applications.