Unsupervised clustering of spontaneous speech documents
Edgar Gonzàlez, Jordi Turmo · 2005
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. 1.