Short-Term Spatio-Temporal Clustering of Sporadic and Concurrent Events

Accurate detection and segmentation of spontaneous multi-party speech is crucial for a variety of applications, including speech acquisition and recognition, as well as higher-level event recognition. However, the highly sporadic nature of spontaneous speech makes this task difficult. Moreover, multi-party speech contains many overlaps. We propose to attack this problem as a multitarget tracking task, using location cues only. In order to best deal with high sporadicity, we propose a novel, generic, short-term clustering algorithm that can track multiple objects for a low computational cost. The proposed approach is online, fully deterministic and can run in real-time. In an application to real meeting data, the algorithm produces high precision speech segmentation.


Year:
2004
Publisher:
Martigny, Switzerland, IDIAP
Keywords:
Note:
Published in ``Proceedings of the 2004 ICASSP-NIST Meeting Recognition Workshop''
Laboratories:




 Record created 2006-03-10, last modified 2018-03-17

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