Attentive Modality Hopping Mechanism for Speech Emotion Recognition

TL;DR

This paper introduces an attention-based modality hopping mechanism that dynamically combines speech, text, and visual data to improve emotion recognition accuracy, outperforming traditional fusion methods.

Contribution

The work proposes a novel attention mechanism that selectively integrates multimodal information for speech emotion recognition, enhancing performance over existing fusion approaches.

Findings

Achieved a 3.65% improvement in weighted accuracy on IEMOCAP dataset.

Demonstrated effective multimodal integration using attention mechanisms.

Validated the approach's superiority over baseline systems.

Abstract

In this work, we explore the impact of visual modality in addition to speech and text for improving the accuracy of the emotion detection system. The traditional approaches tackle this task by fusing the knowledge from the various modalities independently for performing emotion classification. In contrast to these approaches, we tackle the problem by introducing an attention mechanism to combine the information. In this regard, we first apply a neural network to obtain hidden representations of the modalities. Then, the attention mechanism is defined to select and aggregate important parts of the video data by conditioning on the audio and text data. Furthermore, the attention mechanism is again applied to attend important parts of the speech and textual data, by considering other modality. Experiments are performed on the standard IEMOCAP dataset using all three modalities (audio, text, and video). The achieved results show a significant improvement of 3.65% in terms of weighted accuracy compared to the baseline system.