Stochastic Mean-Shift for Speaker Clustering

I. Lapidot
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Abstract

This work is a continuation of our previous work on short segments speaker clustering. We have shown that mean-shift clustering algorithm with probabilistic linear discriminant analysis (PLDA) score as the similarity measure, can be a good approach for this task. While the standard mean-shift clustering algorithm is a deterministic algorithm, in this work we suggest a stochastic version to train the mean-shift. The quality of the clustering is measured by the value K, which is a geometric mean of average cluster purity (ACP) and average speaker purity (ASP). We test the proposed algorithm in the range of 3 to 60 speakers and show that it outperforms the deterministic mean-shift in all cases.
说话人聚类的随机Mean-Shift
这项工作是我们之前关于短段说话人聚类工作的延续。我们已经证明,以概率线性判别分析(PLDA)分数作为相似性度量的mean-shift聚类算法可以很好地解决这一问题。虽然标准mean-shift聚类算法是一种确定性算法,但在这项工作中,我们提出了一种随机版本来训练mean-shift。聚类质量通过K值来衡量,K值是平均聚类纯度(ACP)和平均说话者纯度(ASP)的几何平均值。我们在3到60个说话者的范围内测试了所提出的算法,并表明它在所有情况下都优于确定性mean-shift。
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