Abstract
In multiparty meetings, participants need to predict the end of the speaker’s utterance and who will start speaking next, as well as consider a strategy for good timing to speak next. Gaze behavior plays an important role in smooth turn-changing. This article proposes a prediction model that features three processing steps to predict (I) whether turn-changing or turn-keeping will occur, (II) who will be the next speaker in turn-changing, and (III) the timing of the start of the next speaker’s utterance. For the feature values of the model, we focused on gaze transition patterns and the timing structure of eye contact between a speaker and a listener near the end of the speaker’s utterance. Gaze transition patterns provide information about the order in which gaze behavior changes. The timing structure of eye contact is defined as who looks at whom and who looks away first, the speaker or listener, when eye contact between the speaker and a listener occurs. We collected corpus data of multiparty meetings, using the data to demonstrate relationships between gaze transition patterns and timing structure and situations (I), (II), and (III). The results of our analyses indicate that the gaze transition pattern of the speaker and listener and the timing structure of eye contact have a strong association with turn-changing, the next speaker in turn-changing, and the start time of the next utterance. On the basis of the results, we constructed prediction models using the gaze transition patterns and timing structure. The gaze transition patterns were found to be useful in predicting turn-changing, the next speaker in turn-changing, and the start time of the next utterance. Contrary to expectations, we did not find that the timing structure is useful for predicting the next speaker and the start time. This study opens up new possibilities for predicting the next speaker and the timing of the next utterance using gaze transition patterns in multiparty meetings.
Publisher
Association for Computing Machinery (ACM)
Subject
Artificial Intelligence,Human-Computer Interaction
Cited by
41 articles.
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