Unsupervised validity measures for vocalization clustering
Kuntoro Adi, Kristine E. Sonstrom, Peter M. Scheifele, Michael T. Johnson · IEEE International Conference on Acoustics Speech and Signal Processing · 2008
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.