{"title":"Stochastic Mean-Shift for Speaker Clustering","authors":"I. Lapidot","doi":"10.1109/CISS56502.2023.10089776","DOIUrl":null,"url":null,"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.","PeriodicalId":243775,"journal":{"name":"2023 57th Annual Conference on Information Sciences and Systems (CISS)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 57th Annual Conference on Information Sciences and Systems (CISS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CISS56502.2023.10089776","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
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.