Let’s Go Real Talk: Spoken Dialogue Model for Face-to-Face Conversation
Se Jin Park, Chae Kim, Hyeongseop Rha, Minsu Kim, Joanna H. Hong, Jeong Hun Yeo, Yong Man Ro · 2024
In this paper, we introduce a novel Face-to-Face spoken dialogue model.It processes audio-visual speech from user input and generates audio-visual speech as the response, marking the initial step towards creating an avatar chatbot system without relying on intermediate text.To this end, we newly introduce MultiDialog, the first large-scale multimodal (i.e., audio and visual) spoken dialogue corpus containing 340 hours of approximately 9,000 dialogues, recorded based on the open domain dialogue dataset, TopicalChat.The MultiDialog contains parallel audio-visual recordings of conversation partners acting according to the given script with emotion annotations, which we expect to open up research opportunities in multimodal synthesis.Our Face-to-Face spoken dialogue model incorporates a textually pretrained large language model and adapts it into the audio-visual spoken dialogue domain by incorporating speech-text joint pretraining.Through extensive experiments, we validate the effectiveness of our model in facilitating a face-to-face conversation.