This paper extends upon a previous work using Mean Shift algorithm to perform speaker clustering on i-vectors generated from short speech segments. In this paper we examine the effectiveness of probabilistic linear discriminant analysis (PLDA) scoring as the metric of the mean shift clustering algorithm in the presence of different number of speakers. Our proposed method, combined with k-nearest neighbors (kNN) for bandwidth estimation, yields better and more robust results in comparison to the cosine similarity with fixed neighborhood bandwidth for clustering segments of large number of speakers. In the case of 30 speakers, we achieved evaluation parameter of 72.1 with the PLDA-based mean shift algorithm compared to 65.9 with the cosine-based baseline system.
Cite as: Salmun, I., Opher, I., Lapidot, I. (2016) On the Use of PLDA i-vector Scoring for Clustering Short Segments. Proc. The Speaker and Language Recognition Workshop (Odyssey 2016), 407-414, doi: 10.21437/Odyssey.2016-59
@inproceedings{salmun16_odyssey,
author={Itay Salmun and Irit Opher and Itshak Lapidot},
title={{On the Use of PLDA i-vector Scoring for Clustering Short Segments}},
year=2016,
booktitle={Proc. The Speaker and Language Recognition Workshop (Odyssey 2016)},
pages={407--414},
doi={10.21437/Odyssey.2016-59}
}