Human Emotion Detection from Audio and Video Signals

TL;DR

This paper discusses a model that detects human emotions using audio and video signals to improve human-machine interaction, especially for users with difficulty expressing emotions, and maintains accuracy despite poor video quality.

Contribution

It introduces a multimodal emotion detection model that combines audio and video analysis, enhancing robustness in varied quality conditions and aiding users with communication challenges.

Findings

Effective emotion detection from audio and video signals.

Robustness to poor video quality.

Potential applications for social and assistive technologies.

Abstract

The primary objective is to teach a machine about human emotions, which has become an essential requirement in the field of social intelligence, also expedites the progress of human-machine interactions. The ability of a machine to understand human emotion and act accordingly has been a choice of great interest in today's world. The future generations of computers thus must be able to interact with a human being just like another. For example, people who have Autism often find it difficult to talk to someone about their state of mind. This model explicitly targets the userbase who are troubled and fail to express it. Also, this model's speech processing techniques provide an estimate of the emotion in the case of poor video quality and vice-versa.