Unsupervised Speaker Clustering in a Linear Discriminant Subspace

Abstract

We present an approach for grouping single-speaker speech segments into speaker-specific clusters. Our approach is based on applying the K-means clustering algorithm to a suitable discriminant subspace, where the euclidean distance reflect speaker differences. A core feature of our approach is approximating speaker-conditional statistics, that are not… (More)
DOI: 10.1109/ICMLA.2010.159

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