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 available, with single-speaker segments statistics, which can be evaluated, thus making possible to apply the LDA algorithm for finding the optimal discriminative subspace, using unlabeled data. To illustrate our method, we present examples of clusters generated by our approach when applied to the ICMLA 2010 Speaker Clustering Challenge datasets.