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Fusion Gérard CHOLLET GET-ENST/CNRS-LTCI 46 rue Barrault 75634 PARIS cedex 13

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1 Fusion Gérard CHOLLET GET-ENST/CNRS-LTCI 46 rue Barrault PARIS cedex 13

2 Plan Motivations, Applications Reconnaissance de formes Multi-capteurs Rehaussement du signal Parametres Scores Decisions Conclusions Perspectives

3 Introduction Reconnaissance des formes Pourquoi fusionner ? Que fusionner ? Des signaux issus de capteurs divers, Des parametres mesures sur ces signaux, Des scores calculés par des classificateurs, Des decisions prises par des classificateurs Comment fusionner ?

4 Reconnaissance de formes

5 Fusion de signaux Nombre de capteurs Types de capteurs Identiques ? Nombre de sources Exemples : Réseaux de microphones Stérovision Seïsmographe

6 Fusion de paramètres Issus dun seul capteur Issus de plusieurs capteurs Modèles multi-flux Exemples : Reconnaissance de la parole Réseaux bayésiens

7 Fusion de scores

8 Fusion de décisions

9 Vector Quantization (VQ) best quant. Dictionnaire locuteur 1 Dictionnaire locuteur 2 Dictionnaire locuteur n Bonjour locuteur test Y Dictionnaire locuteur X SOONG, ROSENBERG 1987

10 Hidden Markov Models (HMM) Best path Bonjour locuteur 1 Bonjour locuteur 2 Bonjour locuteur n Bonjour locuteur test Y Bonjour locuteur X ROSENBERG 1990, TSENG 1992

11 Ergodic HMM Best path HMM locuteur 1 HMM locuteur 2 HMM locuteur n Bonjour locuteur test Y HMM locuteur X PORITZ 1982, SAVIC 1990

12 Gaussian Mixture Models (GMM) REYNOLDS 1995

13 HMM structure depends on the application

14 Gaussian Mixture Model Parametric representation of the probability distribution of observations:

15 Gaussian Mixture Models 8 Gaussians per mixture

16 Support Vector Machines and Speaker Verification Hybrid GMM-SVM system is proposed SVM scoring model trained on development data to classify true-target speakers access and impostors access, using new feature representation based on GMMs Modeling Scoring GMM SVM

17 SVM principles X (X) Input space Feature space Separating hyperplans H, with the optimal hyperplan H o HoHo H Class(X)

18 Results

19 Combining Speech Recognition and Speaker Verification. Speaker independent phone HMMs Selection of segments or segment classes which are speaker specific Preliminary evaluations are performed on the NIST extended data set (one hour of training data per speaker) Some developments were done during a 6 weeks workshop (SuperSID) during summer 2002

20 SuperSID experiments

21 GMM with cepstral features

22 Selection of nasals in words in -ing being everything getting anything thing something things going

23 Fusion

24 Fusion results

25 Audio-Visual Identity Verification A person speaking in front of a camera offers 2 modalities for identity verification (speech and face). The sequence of face images and the synchronisation of speech and lip movements could be exploited. Imposture is much more difficult than with single modalities. Many PCs, PDAs, mobile phones are equiped with a camera. Audio-Visual Identity Verification will offer non-intrusive security for e-commerce, e-banking,…

26 Examples of Speaking Faces Sequence of digits (PIN code)Free text

27 Fusion of Speech and Face (from thesis of Conrad Sanderson, aug. 2002)

28 1.Acquisition of biometric signals for each modality 2.Scores are computed for each modality 3.Fusion of scores and decision Insecure Network Distant server: 1.Access to private data 2.Secured transactions An illustration

29 Conclusions and Perspectives Speech is often the only usable biometric modality (over the telephone network). Interactive Voice Servers may use both text dependent and text independent approaches for improved verification accuracy. Evaluation campaigns and research workshops are efficient means to stimulate progress. Most PCs, PDAs and Mobile Phones will be equipped with cameras. Audio-Visual Identity Verification should find applications in e-Banking, e-Commerce, ….

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