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

arXiv: https://arxiv.org/abs/2409.07901 · HTML: https://mlsanity.com/paper/2409.07901/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Sep 2024.

## Built on by (most cited first, 6 of 6)

- [Survey of Cultural Awareness in Language Models: Text and Beyond](https://mlsanity.com/paper/2411.00860/) (2024)
- [UDDETTS: Unifying Discrete and Dimensional Emotions for Controllable Emotional Text-to-Speech](https://mlsanity.com/paper/2505.10599/) (2025)
- [Emotion-Aware Speech Generation with Character-Specific Voices for Comics](https://mlsanity.com/paper/2509.15253/) (2025)
- [EmoHeal: An End-to-End System for Personalized Therapeutic Music Retrieval from Fine-grained Emotions](https://mlsanity.com/paper/2509.15986/) (2025)
- [Modeling Induced Pleasure through Cognitive Appraisal Prediction via Multimodal Fusion](https://mlsanity.com/paper/2604.23753/) (2026)
- [BurnRiSc: Toward Non-Invasive Burnout Screening in Open Source from Public Repository Signals](https://mlsanity.com/paper/2609.19422/) (2026)

## Abstract (arXiv)

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.