Unsupervised validity measures for vocalization clustering

Author

Adi, Kuntoro ; Sonstrom, Kristine E. ; Scheifele, Peter M. ; Johnson, Michael T.

Author_Institution

Dept. of Electr. & Comput. Eng., Marquette Univ., Milwaukee, WI

fYear

2008

fDate

March 31 2008-April 4 2008

Firstpage

4377

Lastpage

4380

Abstract

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.

Keywords

acoustic signal processing; biocommunications; pattern clustering; speech processing; Beluga whale vocalization clustering; cross-data dissimilarity values; speech data set; unsupervised speech-speaker cluster validity; vocalization clustering; Acoustic applications; Animals; Clustering algorithms; Electric variables measurement; Humans; Indexing; Partitioning algorithms; Speech analysis; Statistics; Whales; dissimilarity value; speech/speaker clustering; unsupervised validity; validation of classifiers;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech and Signal Processing, 2008. ICASSP 2008. IEEE International Conference on

Conference_Location

Las Vegas, NV

ISSN

1520-6149

Print_ISBN

978-1-4244-1483-3

Electronic_ISBN

1520-6149

Type

conf

DOI

10.1109/ICASSP.2008.4518625

Filename

4518625