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
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 ident
ification. 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%).
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