Human Emotion Detection from Audio and Video Signals
This work addresses emotion detection for enhanced social intelligence and human-machine interaction, though it appears incremental as it builds on existing multimodal techniques without claiming major breakthroughs.
The paper tackles the problem of detecting human emotions from audio and video signals to improve human-machine interactions, with a focus on aiding individuals like those with autism who struggle to express their emotions, but it does not provide concrete numerical results.
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.