FaceChat: An Emotion-Aware Face-to-face Dialogue Framework
This work addresses the problem of enhancing user experience in dialogue systems for applications like counseling and customer service, though it appears incremental as it combines existing technologies into a new framework.
The authors tackled the lack of multimodal emotional interaction in dialogue systems by developing FaceChat, a web-based framework that integrates NLP, computer vision, and speech processing to enable emotionally-sensitive face-to-face conversations, with the code made publicly available.
While current dialogue systems like ChatGPT have made significant advancements in text-based interactions, they often overlook the potential of other modalities in enhancing the overall user experience. We present FaceChat, a web-based dialogue framework that enables emotionally-sensitive and face-to-face conversations. By seamlessly integrating cutting-edge technologies in natural language processing, computer vision, and speech processing, FaceChat delivers a highly immersive and engaging user experience. FaceChat framework has a wide range of potential applications, including counseling, emotional support, and personalized customer service. The system is designed to be simple and flexible as a platform for future researchers to advance the field of multimodal dialogue systems. The code is publicly available at https://github.com/qywu/FaceChat.