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Blind Speaker Clustering is a task within speech technology, where we have a collection of speech recordings (utterances), and the goal is to identify which utterances belong to the same speakers. To aid the clustering process in this task, we performed preprocessing steps such as feature selection and Principal Component Analysis (PCA); still, the choice of clustering method is not a trivial one. To find the best performing algorithm, we tested standard methods such as k-means (or hard c-means, HCM) and fuzzy c-means (FCM) as well as several improved versions of FCM. In the end, we were able to achieve the best performance using probabilistic-possibilistic mixture partitions. The obtained purity score of 83.9% is significantly higher than the baseline score of 46.9%.

Application of Fuzzy and Possibilistic c-Means Clustering Models in Blind Speaker Clustering