Incremental On-Line Clustering of Speakers' Short Segments

This paper deals with clustering of speakers’ short segments, in a scenario where additional segments continue to arrive and should be constantly clustered together with previous segments that were already clustered. In realistic applications, it is not possible to cluster all segments every time a new segment arrives. Hence, incremental clustering is applied in an on-line mode. New segments can either belong to existing speakers, therefore, have to be assigned to one of the existing clusters, or they could belong to new speakers and thus new clusters should be formed. In this work we show that if there are enough segments per speaker in the off-line initial clustering process, it constitutes a good starting point for the incremental on-line clustering. In this case, incremental online clustering can be successfully applied based on the previously proposed mean-shift clustering algorithm with PLDA score as a similarity measure and with k-nearest neighbors (kNN) neighborhood selection.