Technical Research and Application Analysis of Multimodal Emotion Recognition

In the digital era, social software has become integral to everyday life and work, serving as platforms for users to share content and interact. The personal opinions and emotions embedded in these interactions significantly impact the real world, shaping trends and opinions. This backdrop has catapulted emotion recognition into a critical research area within artificial intelligence. Traditional monomodal emotion recognition often falls short in accurately predicting user emotions, paving the way for multimodal emotion recognition. This approach, leveraging a comprehensive analysis of voice, visual, and textual data, offers a more nuanced understanding of human emotions. This paper explores the pivotal technologies, system applications, challenges, and future directions of multimodal emotion recognition. It begins by introducing the concept and significance of multimodal emotion recognition in AI. The subsequent section delves into data processing, encompassing the collection and integration of multimodal data, and examining how these processes affect emotion recognition accuracy. The third section reviews algorithms and models in multimodal emotion recognition, discussing the advancements and hurdles in modal fusion technology. Case studies illustrate the impact of these technologies in practical scenarios. The final section outlines the primary challenges, future trends, and potential research avenues in multimodal emotion recognition technology.