Unsupervised clustering of spontaneous speech documents

This paper presents an unsupervised method for clustering spontaneous speech documents. The approach uses a hierarchical algorithm to automatically determine the number of clusters and a starting model for a subsequent iterative algorithm. We have evaluated this method on the Switchboard corpus and compared it to a set of supervised and other unsupervised methods. The results show that our method significantly outperforms the rest of the approaches.

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