A Multimodal Emotion Recognition System: Integrating Facial Expressions, Body Movement, Speech, and Spoken Language

TL;DR

This paper introduces a multimodal emotion recognition system that combines facial expressions, body movement, speech, and spoken language to provide an objective, comprehensive, and reliable tool for psychological assessments, improving diagnostic accuracy.

Contribution

The work presents a novel integrated system that combines multiple modalities for emotion recognition, addressing limitations of subjective human evaluations in psychological practice.

Findings

System demonstrates improved emotional state assessment accuracy.

Preliminary testing shows reliable emotional insights in simulated real-world conditions.

Reduces risk of misdiagnosis and overdiagnosis in clinical settings.

Abstract

Traditional psychological evaluations rely heavily on human observation and interpretation, which are prone to subjectivity, bias, fatigue, and inconsistency. To address these limitations, this work presents a multimodal emotion recognition system that provides a standardised, objective, and data-driven tool to support evaluators, such as psychologists, psychiatrists, and clinicians. The system integrates recognition of facial expressions, speech, spoken language, and body movement analysis to capture subtle emotional cues that are often overlooked in human evaluations. By combining these modalities, the system provides more robust and comprehensive emotional state assessment, reducing the risk of mis- and overdiagnosis. Preliminary testing in a simulated real-world condition demonstrates the system's potential to provide reliable emotional insights to improve the diagnostic accuracy. This work highlights the promise of automated multimodal analysis as a valuable complement to traditional psychological evaluation practices, with applications in clinical and therapeutic settings.