This paper investigates the task of linking speakers across multiple recordings, which can be accomplished by speaker clustering. Various aspects are considered, such as computational complexity, on/offline approaches, and evaluation measures but also speaker recognition approaches. It has not been the aim of this study to optimize clustering performance, but as an experimental exercise, we perform speaker linking on all '1conv-4w' conversation sides of the NIST-2006 evaluation data set. This set contains 704 speakers in 3835 conversation sides. Using both on-line and off-line algorithms, equal-purity figures of about 86% are obtained.
Cite as: Leeuwen, D.v. (2010) Speaker linking in large data sets. Proc. The Speaker and Language Recognition Workshop (Odyssey 2010), paper 35
@inproceedings{leeuwen10_odyssey,
author={David van Leeuwen},
title={{Speaker linking in large data sets}},
year=2010,
booktitle={Proc. The Speaker and Language Recognition Workshop (Odyssey 2010)},
pages={paper 35}
}