Speaker Linking and Applications Using Non-Parametric Hashing Methods

Douglas E. Sturim, William M. Campbell


Large unstructured audio data sets have become ubiquitous and present a challenge for organization and search. One logical approach for structuring data is to find common speakers and link occurrences across different recordings. Prior approaches to this problem have focused on basic methodology for the linking task. In this paper, we introduce a novel trainable non-parametric hashing method for indexing large speaker recording data sets. This approach leads to tunable computational complexity methods for speaker linking. We focus on a scalable clustering method based on hashing — canopy-clustering. We apply this method to a large corpus of speaker recordings, demonstrate performance tradeoffs, and compare to other hashing methods.


DOI: 10.21437/Interspeech.2016-468

Cite as

Sturim, D.E., Campbell, W.M. (2016) Speaker Linking and Applications Using Non-Parametric Hashing Methods. Proc. Interspeech 2016, 2170-2174.

Bibtex
@inproceedings{Sturim+2016,
author={Douglas E. Sturim and William M. Campbell},
title={Speaker Linking and Applications Using Non-Parametric Hashing Methods},
year=2016,
booktitle={Interspeech 2016},
doi={10.21437/Interspeech.2016-468},
url={http://dx.doi.org/10.21437/Interspeech.2016-468},
pages={2170--2174}
}