We present RoomReader, a corpus of multimodal, multiparty conversational interactions in which participants followed a collaborative student-tutor scenario designed to elicit spontaneous speech. The corpus was developed within the wider RoomReader Project to explore multimodal cues of conversational engagement and behavioural aspects of collaborative interaction in online environments. However, the corpus can be used to study a wide range of phenomena in online multimodal interaction. The publicly-shared corpus consists of over 8 hours of video and audio recordings from 118 participants in 30 gender-balanced sessions, in the “in-the-wild” online environment of Zoom. The recordings have been edited, synchronised, and fully transcribed. Student participants have been continuously annotated for engagement with a novel continuous scale. We provide questionnaires measuring engagement and group cohesion collected from the annotators, tutors and participants themselves. We also make a range of accompanying data available such as personality tests and behavioural assessments. The dataset and accompanying psychometrics present a rich resource enabling the exploration of a range of downstream tasks across diverse fields including linguistics and artificial intelligence. This could include the automatic detection of student engagement, analysis of group interaction and collaboration in online conversation, and the analysis of conversational behaviours in an online setting.

The timings of spoken response offsets in human dialogue have been shown to vary based on contextual elements of the dialogue. We propose neural models that simulate the distributions of these response offsets, taking into account the response turn as well as the preceding turn. The models are designed to be integrated into the pipeline of an incremental spoken dialogue system (SDS). We evaluate our models using offline experiments as well as human listening tests. We show that human listeners consider certain response timings to be more natural based on the dialogue context. The introduction of these models into SDS pipelines could increase the perceived naturalness of interactions.