Short-Term Spatio-Temporal Clustering Applied to Multiple Moving Speakers

Distant microphones permit to process spontaneous multi-party speech with very little constraints on speakers, as opposed to close-talking microphones. Minimizing the constraints on speakers permits a large diversity of applications, including meeting summarization and browsing, surveillance, hearing aids, and more natural human-machine interaction. Such applications of distant microphones require to determine where and when the speakers are talking. This is inherently a multisource problem, because of background noise sources, as well as the natural tendency of multiple speakers to talk over each other. Moreover, spontaneous speech utterances are highly discontinuous, which makes difficult to track the multiple speakers with classical filtering approaches, such as Kalman Filtering of Particle Filters. As an alternative, this paper proposes a probabilistic framework to determine the trajectories of multiple moving speakers in the short-term only -- i.e. only while they speak. Instantaneous location estimates that are close in space and time are grouped into ``short-term clusters'' in a principled manner. Each short-term cluster determines the precise start and end times of an utterance, and a short-term spatial trajectory. Contrastive experiments clearly show the benefit of using short-term clustering, on real indoor recordings with seated speakers in meetings, as well as multiple moving speakers.