1. Bayesian Networks Toolkit Objective: free C++ toolkit to manipulate and experiment with Bayesian Networks Status: – Released at the INRIA Gforge: BaNeTo – A simple C++ interface has been designed to access the underlying functionalities – But: Continuous training in some cases with hidden variables still buggy ! – Issue: No more engineer to fix this bug…

2. Missing Data Recognition Achievements during the last period: – Design and implementation of a new (theoretically more correct) marginalisation procedure – Design and implementation of a new masks model with temporal and frequency constraints – Validation of the proposed approach on the Aurora2 database

3. Missing Data Recognition: New marginalisation Local SNR data masked Local SNR > 0dB => data unmasked This translates into: with

4. Missing Data Recognition: New masks model Masks model = HMM – Observation pdfs (GMMs) = p(y|m,state) – Transition probabilities = Frequency constraints Frequency constraints: – Clustering of the space covered by training masks – Observation pdf: m = one of this VQ codebook – First time the correlation between the feature dimensions are modeled !

5. Missing Data Recognition: New masks model

6. Missing Data Recognition: Validation on Aurora2 test A

7. Missing Data Recognition: Validation on Aurora2 test B