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Voic
ePIN
.com
ul.K
raku
sa 1
1,
30-5
35
tel: 7
26 5
03 4
03
mail:in
fo@
voic
epin
.com
Voice Biometrics
No sensors required
Various solution scenarios
Cheap
Comfortable
Natural
Known basic applications:
Identity verification
• access controll
• biometric PIN
• password reset (30%-40% cases)
Identity identification
• fraud detection
• service personlization
• other
HybridPrompted
Textindependent
Textdependent
How voice authentication works ?
Voice signal
Voice signalspectrogram
Voice signalsmoothed spectrogram
Voice features MFCCMel-Freq Cepstral Coeff.
50 100 150 200 250 300 350 400
2
4
6
8
10
Performance• FRR - False Rejection Rate, % • FAR - False Acceptance Rate, % • EER - Equal Error Rate, % when the decision treshold fixed as to assure FAR = FRR.
• Accuracy - %, percentage of (any) correctdecisions depends on the evaluationscenario and decision threshold settings
• Example of the performance specificationFAR < 0.1%, and FRR< 5%
• Statistical significance: How to assess the risk of rarely
occuring phenomena ?
Security vs usability issue
% errors
Decision threshold
Attacks (FAR)Rejections(FRR)
EER
password, 123, 0000, love
NA7;zSrluz, Mj[LAX}i]O, 9622535008, 594772359571
Evaluation scenario
Ellen SierraVoiceprint
False acceptances % (succesful attacks)
False rejections % (unsuccesful genuine verification attempts)
Decision score (-100, 100)
Learning Methods
Statistical - GMM
SVM – Supervectors
Factor Analysis (i-Vectors)
Deep learning (DNN)
GMM UBM MAP Framework
GMM MAP Adaptation
Supervector Factor Analysis
Supervector Factor Analysis
Deep Learning
Deep Learning results – DET plot
