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Authors: M. Chenafa 1 ; D. Istrate 1 ; V. Vrabie 2 and M. Herbin 2

Affiliations: 1 RMSE, ESIGETEL, France ; 2 CReSTIC, Université de Reims Champagne-Ardenne, France

ISBN: 978-989-8111-18-0

Keyword(s): Biometrics, Speaker recognition, Speech recognition, Decision fusion, GMM/UBM.

Related Ontology Subjects/Areas/Topics: Applications ; Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Biometrics ; Biometrics and Pattern Recognition ; Data Manipulation ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Methodologies and Methods ; Multimedia ; Multimedia Signal Processing ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Soft Computing ; Telecommunications

Abstract: Biometrics systems have gained in popularity for the automatic identification of persons. The use of the voice as a biometric characteristic offers advantages such as: is well accepted, it works with regular microphones, the hardware costs are reduced, etc. However, the performance of a voice-based biometric system easily degrades in the presence of a mismatch between training and testing conditions due to different factors. This paper presents a new speaker recognition system based on decision fusion. The fusion is based on two identification systems: a speaker identification system (text-independent) and a keywords identification system (speaker-independent). These systems calculate the likelihood ratios between the model of a test signal and the different models of the database. The fusion uses these results to identify the couple speaker/password corresponding to the test signal. A verification system is then applied on a second test signal in order to confirm or infirm the identi fication. The fusion step improves the false rejection rate (FRR) from 21, 43% to 7, 14% but increase also the false acceptation rate (FAR) from 21, 43% to 28, 57%. The verification step makes however a significant improvement on the FAR (from 28, 57% to 14.28%) while it keeps constant the FRR (to 7, 14%). (More)

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Paper citation in several formats:
Chenafa M.; Istrate D.; Vrabie V.; Herbin M. and (2008). SPEAKER RECOGNITION USING DECISION FUSION.In Proceedings of the First International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2008) ISBN 978-989-8111-18-0, pages 267-272. DOI: 10.5220/0001065502670272

@conference{biosignals08,
author={M. Chenafa and D. Istrate and V. Vrabie and M. Herbin},
title={SPEAKER RECOGNITION USING DECISION FUSION},
booktitle={Proceedings of the First International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2008)},
year={2008},
pages={267-272},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001065502670272},
isbn={978-989-8111-18-0},
}

TY - CONF

JO - Proceedings of the First International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2008)
TI - SPEAKER RECOGNITION USING DECISION FUSION
SN - 978-989-8111-18-0
AU - Chenafa, M.
AU - Istrate, D.
AU - Vrabie, V.
AU - Herbin, M.
PY - 2008
SP - 267
EP - 272
DO - 10.5220/0001065502670272

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