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 th...

Full description

Saved in:
Bibliographic Details
Main Authors: Adi, K., Sonstrom, K.E., Scheifele, P.M., Johnson, M.T.
Format: Conference Proceeding
Language:English
Subjects:
Acoustic applications
Animals
Clustering algorithms
dissimilarity value
Electric variables measurement
Humans
Indexing
Partitioning algorithms
Speech analysis
speech/speaker clustering
Statistics
unsupervised validity
validation of classifiers
Whales
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:1520-6149
2379-190X
DOI:10.1109/ICASSP.2008.4518625