Multi-Modal Emotion recognition on IEMOCAP Dataset using Deep Learning

TL;DR

This paper presents a multi-modal deep learning approach for emotion recognition on the IEMOCAP dataset, integrating speech, text, and motion capture data to improve accuracy and robustness over prior speech-only methods.

Contribution

It introduces the first multi-modal neural network model for emotion recognition on IEMOCAP, combining diverse data sources for enhanced performance.

Findings

Achieved improved accuracy over speech-only models

Demonstrated effectiveness of multi-modal data fusion

Provided insights into multimodal emotion cues

Abstract

Emotion recognition has become an important field of research in Human Computer Interactions as we improve upon the techniques for modelling the various aspects of behaviour. With the advancement of technology our understanding of emotions are advancing, there is a growing need for automatic emotion recognition systems. One of the directions the research is heading is the use of Neural Networks which are adept at estimating complex functions that depend on a large number and diverse source of input data. In this paper we attempt to exploit this effectiveness of Neural networks to enable us to perform multimodal Emotion recognition on IEMOCAP dataset using data from Speech, Text, and Motion capture data from face expressions, rotation and hand movements. Prior research has concentrated on Emotion detection from Speech on the IEMOCAP dataset, but our approach is the first that uses the multiple modes of data offered by IEMOCAP for a more robust and accurate emotion detection.