Improving Speech Emotion Recognition Through Focus and Calibration Attention Mechanisms

TL;DR

This paper introduces Focus-Attention and Calibration-Attention mechanisms to enhance deep learning models for Speech Emotion Recognition by better aligning attention with signal amplitude and context, leading to improved accuracy.

Contribution

The paper proposes novel Focus-Attention and Calibration-Attention mechanisms that improve attention alignment and context utilization in speech emotion recognition models.

Findings

Significant performance improvement over state-of-the-art methods

Effective focus on high amplitude regions in speech signals

Enhanced context utilization through calibration mechanism

Abstract

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.