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Becars: an Automatic Speaker Verification system Chafic Mokbel (1) Raphaël Blouet (2) Eduardo Sanchez Soto (2) Gérard Chollet (2) (1) (1)University of.

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Presentation on theme: "Becars: an Automatic Speaker Verification system Chafic Mokbel (1) Raphaël Blouet (2) Eduardo Sanchez Soto (2) Gérard Chollet (2) (1) (1)University of."— Presentation transcript:

1 Becars: an Automatic Speaker Verification system Chafic Mokbel (1) Raphaël Blouet (2) Eduardo Sanchez Soto (2) Gérard Chollet (2) (1) (1)University of Balamand, El Koura, Libanon (2) (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 lENST 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 Bla-bla SECURED SPACE Biometry: something that you are Something that you know Something that you have

6 Automatic Speaker Verification Claimed Identity Automatic Speaker Verification System 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] AcceptationRejection

8 Parameters : Gaussians Mixture Model

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 lEM partitionnement donnant une quantité de données suffisante par classe

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


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