Attention has become one of the most commonly used mechanisms in deep learning approaches. The attention mechanism can help the system focus more on the feature space's critical regions. For example, high amplitude regions can play an important role for Speech Emotion Recognition (SER). In this paper, we identify misalignments between the attention and the signal amplitude in the existing multi-head self-attention. To improve the attention area, we propose to use a Focus-Attention (FA) mechanism and a novel Calibration-Attention (CA) mechanism in combination with the multi-head self-attention. Through the FA mechanism, the network can detect the largest amplitude part in the segment. By employing the CA mechanism, the network can modulate the information flow by assigning different weights to each attention head and improve the utilization of surrounding contexts. To evaluate the proposed method, experiments are performed with the IEMOCAP and RAVDESS datasets. Experimental results show that the proposed framework significantly outperforms the state-of-the-art approaches on both datasets.

本文提出了一种改进的注意力机制，结合了多头自我注意力、焦点注意力和校准注意力，来定位语音情感识别中信号幅度的重要部分，并调节注意力权值以提高周围内容的利用率，实验结果表明该方法在IEMOCAP和RAVDESS数据集上都优于现有最先进方法。

通过聚焦和校准关注机制改进语音情绪识别