In human-computer interaction, Speech Emotion Recognition (SER) plays an essential role in understanding the user's intent and improving the interactive experience. While similar sentimental speeches own diverse speaker characteristics but share common antecedents and consequences, an essential challenge for SER is how to produce robust and discriminative representations through causality between speech emotions. In this paper, we propose a Gated Multi-scale Temporal Convolutional Network (GM-TCNet) to construct a novel emotional causality representation learning component with a multi-scale receptive field. GM-TCNet deploys a novel emotional causality representation learning component to capture the dynamics of emotion across the time domain, constructed with dilated causal convolution layer and gating mechanism. Besides, it utilizes skip connection fusing high-level features from different gated convolution blocks to capture abundant and subtle emotion changes in human speech. GM-TCNet first uses a single type of feature, mel-frequency cepstral coefficients, as inputs and then passes them through the gated temporal convolutional module to generate the high-level features. Finally, the features are fed to the emotion classifier to accomplish the SER task. The experimental results show that our model maintains the highest performance in most cases compared to state-of-the-art techniques.

本文提出一种Gated Multi-scale Temporal Convolutional Network (GM-TCNet)情感因果表示学习模型, 通过构建多尺度感受野的情感因果表示学习模块来捕捉情感动态, 并利用跳跃连接融合不同门卷积块中的高级特征以捕捉人类语音中丰富微妙的情感变化进行语音情感识别, 与最先进的技术相比具有更高的性能表现。

基于情感因果关系的门控多尺度时间卷积网络用于语音情感识别