Unsupervised Speaker Clustering in a Linear Discriminant Subspace
Theodoros Giannakopoulos and Sergios Petridis
In: ICMLA 2010, 12 -14 Dec 2010, Washington, D.C., USA.
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.
|EPrint Type:||Conference or Workshop Item (Paper)|
|Additional Information:||1st price in the ICMLA 2010 speaker clustering challenge|
|Project Keyword:||Project Keyword UNSPECIFIED|
|Deposited By:||Sergios Petridis|
|Deposited On:||17 March 2011|