Short-Term Spatio-Temporal Clustering of Sporadic and Concurrent Events
| Type of publication: | Idiap-RR |
| Citation: | lathoud-rr-04-14 |
| Number: | Idiap-RR-14-2004 |
| Year: | 2004 |
| Institution: | IDIAP |
| Address: | Martigny, Switzerland |
| Note: | Published in ``Proceedings of the 2004 ICASSP-NIST Meeting Recognition Workshop'' |
| Abstract: | 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. |
| Userfields: | ipdinar={2004}, ipdmembership={speech, vision}, language={English}, |
| Keywords: | |
| Projects: |
Idiap |
| Authors: | |
| Crossref by |
lathoud04a |
| Added by: | [UNK] |
| Total mark: | 0 |
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