Unsupervised validity measures for vocalization clustering

This paper describes unsupervised speech/speaker cluster validity measures based on a dissimilarity metric, for the purpose of estimating the number of clusters in a speech data set as well as assessing the consistency of the clustering procedure. The number of clusters is estimated by minimizing the cross-data dissimilarity values, while algorithm consistency is evaluated by calculating the dissimilarity values across multiple experimental runs. The method is demonstrated on the task of Beluga whale vocalization clustering.

• Type: Conference
• Year: 2008
• Where: ICASSP
• Authors: Kuntoro Adi, Kristine E. Sonstrom, Peter M. Scheifele, Michael T. Johnson
• Keywords: Cluster Validity Measures, Dissimilarity Values, ICASSP 2008, Signal Processing, Whale Vocalization Clustering