1. Becars: an Automatic Speaker Verification system
Chafic Mokbel(1)
Raphaël Blouet(2)
Eduardo Sanchez Soto(2)
Gérard Chollet(2)
University of Balamand, El Koura, Libanon
(2) École Nationale Supérieure des Télécommunications, Paris, France

2. Outline Context and aim of the work Automatic identification
Biometric identification
Automatic Speaker Verification
Applications
State of the art – research directions
Conclusion / discussion

3. Context and aim of the work
Collaboration between the University of Balamamand and l’ENST in the context of the Cedar project a French-Libanese cooperation framework
Setting up and distribution of a gnu software for Automatic speaker Verification
Participation of both sites to the NIST 2004 speaker verification evaluation
Standard in evaluation of state-of-the-art performances

4. Automatic identification
Why ?
Secure access to sensitive data, offices or services
Automatic adaptation of softwares or services to clients…
How ? [S. Liu and M. Silverman 2001.]
“Something that you have” : keys, cards…
“Something that you know” : PIN code, password…
“Something that you are” : Biometric Authentication

5. Biometric identification
Something that you know
Something that you have
Biometry: something that you are
SECURED
SPACE
Bla-bla

6. Automatic Speaker Verification
Verification System
Claimed Identity
Acceptation
Rejection
Speech processing
Biometric Technology

7. State of the art – research directions (1)
Hypothesis Testing:
Y, Acoustic Parameters Space:
Mel Filter Bank Cepstal Coefficient (mfcc).
Speaker Modelization : Gaussian Mixture Model (GMM), [Reynolds, 1994]
Acceptation
Rejection

8. Gaussians Mixture Model
Parameters :

9. State of the art – research directions (3)
world model,
speaker independent,
train with all available speaker, using the algorithm EM .
client model,
Obtained as an adaptation of ,
MAP with a prior distribution
MLLR with a transform function
Unified approach

10. Adaptation
Degré de liberté variable  Partitionnement variable des distributions
Après chaque étape E de l’EM  partitionnement donnant une quantité de données suffisante par classe 12 9 17 6 23 21 33 56

11. Hierarchical - MLLR adapted System

12. Conclusion / discussion
Becars provides a state-of –the-art software for GMM parameters estimation
Speed up development and research purposes of new site wishing to participate to the NIST evaluation
Enhance software quality by users’ feedback