GeSTICS: A Multimodal Corpus for Studying Gesture Synthesis in Two-party Interactions with Contextualized Speech

Generating natural co-speech gestures and facial expressions for effective human-agent interactions requires modeling the intricate interplay between verbal, non-verbal, and contextual cues observed in dyadic human communication. Two types of contextual cues are of particular interest: (1) individual factors of the interlocutors, such as their demographic attributes, and (2) situational factors, like the outcome of a preceding event. To facilitate their study, we introduce the GeSTICS Dataset, a novel multimodal corpus comprising 9,853 questions and 10,460 answers from audiovisual recordings of post-game sports interviews by 147 interviewees. The dataset contains speech data, including textual transcriptions, lexical descriptors, and acoustic features, as well as visual data encompassing the interviewee's body pose and facial expressions, with an emphasis on capturing these modalities during both the question-listening and answering phases of the interview. Furthermore, GeSTICS incorporates metadata about individual factors, such as the age and cultural background of the interviewees, and situational factors, like the results of the games, which are often overlooked in existing multimodal datasets. Our preliminary analysis of GeSTICS reveals that the effects of speech features, such as loudness and lexical choice, on the production of co-speech gestures in both speaking and listening phases are moderated by situational factors and the interviewee's individual factors. GeSTICS is designed to enhance the generation of realistic nonverbal behaviors in virtual agents, animated characters, and human-robot interaction systems, thus contributing to more engaging and effective human-agent communication. The analysis code and the dataset are available at https://gkebe.github.io/gestics/.