Bridging Discrete and Continuous: A Multimodal Strategy for Complex Emotion Detection

TL;DR

This paper introduces a multimodal framework that maps human emotions into a continuous Valence-Arousal-Dominance space, enabling more nuanced emotion recognition from facial expressions, voice, and transcripts, evaluated on Chinese media data.

Contribution

It presents a novel multimodal approach that transitions from discrete to continuous emotion modeling using VAD space and K-means clustering, enhancing emotional diversity and recognition accuracy.

Findings

Effective transformation between discrete and continuous emotion models

Achieved diverse and comprehensive emotion vocabulary

Maintained high accuracy in emotion recognition

Abstract

In the domain of human-computer interaction, accurately recognizing and interpreting human emotions is crucial yet challenging due to the complexity and subtlety of emotional expressions. This study explores the potential for detecting a rich and flexible range of emotions through a multimodal approach which integrates facial expressions, voice tones, and transcript from video clips. We propose a novel framework that maps variety of emotions in a three-dimensional Valence-Arousal-Dominance (VAD) space, which could reflect the fluctuations and positivity/negativity of emotions to enable a more variety and comprehensive representation of emotional states. We employed K-means clustering to transit emotions from traditional discrete categorization to a continuous labeling system and built a classifier for emotion recognition upon this system. The effectiveness of the proposed model is evaluated using the MER2024 dataset, which contains culturally consistent video clips from Chinese movies and TV series, annotated with both discrete and open-vocabulary emotion labels. Our experiment successfully achieved the transformation between discrete and continuous models, and the proposed model generated a more diverse and comprehensive set of emotion vocabulary while maintaining strong accuracy.