AI Models for Emotion Recognition in Video and Audio Data.

Abstract: The automation of emotion recognition using Artificial Intelligence (AI) has seen great interest in the past decade as a step towards making machines more sympathetic with humans emotions. In which way the AI model identifies people emotions through their audio-visual data is conceptually outlined in this paper mainly focusing on design on some theoretical foundations. It describes the integration of video based facial expression analysis and audio based speech tone recognition to form a multimodal emotion recognition system. In the conceptual framework, phases like data pre-processing, feature extraction, model building and multimodal fusion have been introduced. As a supplement, it emphasizes theoretical performance benefits, potential risks such as ethical bias and interpretability, and use-cases in associated HCI, learning and mental health areas. The research indicates that when video and audio data is brought together, the emotional intelligence of AI is given a conceptual boost, thereby laying the groundwork for emotionally aware computing.