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We present the automatic forensic voice comparison system SPES which is developed in cooperation with the Federal Criminal Police Office of Germany (Bundeskriminalamt - BKA), the University of Applied Sciences Koblenz and the Department of Phonetics, University of Trier, Germany. The system is based on the classical GMM-UBM framework based on MAP adaption as described by Reynolds et al. This GMM-UBM framework is extended by creating specific background models for each suspect recording based on the similarity of speakers. The less general and more specific background models lead to better discrimination ability and more reliable results. We show how the usage of recording adapted background models outperforms the standard GMM-UBM approach in different recording conditions.
Author (s): Becker, Timo; Jessen, Michael; Alsbach, Sebastian; Broß, Franz; Meier, Torsten
Affiliation:
Federal Criminal Police Office, Germany; University of Applied Sciences Koblenz, Germany
(See document for exact affiliation information.)
Publication Date:
2010-06-06
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Session subject:
Speech and Forensics - Automated Systems
Permalink: https://aes2.org/publications/elibrary-page/?id=15497
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Becker, Timo; Jessen, Michael; Alsbach, Sebastian; Broß, Franz; Meier, Torsten; 2010; Automatic Forensic Voice Comparison Using Recording Adapted Background Models [PDF]; Federal Criminal Police Office, Germany; University of Applied Sciences Koblenz, Germany; Paper 6-2; Available from: https://aes2.org/publications/elibrary-page/?id=15497
Becker, Timo; Jessen, Michael; Alsbach, Sebastian; Broß, Franz; Meier, Torsten; Automatic Forensic Voice Comparison Using Recording Adapted Background Models [PDF]; Federal Criminal Police Office, Germany; University of Applied Sciences Koblenz, Germany; Paper 6-2; 2010 Available: https://aes2.org/publications/elibrary-page/?id=15497
@article{becker2010automatic,
author={becker timo and jessen michael and alsbach sebastian and broß franz and meier torsten},
journal={journal of the audio engineering society},
title={automatic forensic voice comparison using recording adapted background models},
year={2010},
number={6-2},
month={june},}