1 Cours parole du 9 Mars 2005 enseignants: Dr. Dijana Petrovska-Delacrétaz et Gérard Chollet Reconnaissance du locuteur 1. Introduction, Historique, Domaines d’applications 2. Les indices de l’identité dans la parole 3. Vérification du locuteur 1. Théorie de la decision 2. Dépendante / Indépendante du texte 4. L’imposture vocale 5. Vérification audio-visuelle de l’identité 6. Evaluations 7. Conclusions
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Cours parole du 9 Mars 2005 enseignants: Dr. Dijana Petrovska-Delacrétaz et Gérard Chollet
Cours parole du 9 Mars 2005 enseignants: Dr. Dijana Petrovska-Delacrétaz et Gérard Chollet. Reconnaissance du locuteur Introduction, Historique, Domaines d’applications Les indices de l’identité dans la parole Vérification du locuteur Théorie de la decision - PowerPoint PPT Presentation
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1
Cours parole du 9 Mars 2005enseignants: Dr. Dijana Petrovska-Delacrétaz
et Gérard Chollet
Reconnaissance du locuteur
1. Introduction, Historique, Domaines d’applications 2. Les indices de l’identité dans la parole3. Vérification du locuteur
1. Théorie de la decision2. Dépendante / Indépendante du texte
4. L’imposture vocale5. Vérification audio-visuelle de l’identité6. Evaluations7. Conclusions
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Why should a computer recognize who is speaking ?
• Protection of individual property (habitation, bank account, personal data, messages, mobile phone, PDA,...)
• Limited access (secured areas, data bases)• Personalization (only respond to its master’s voice)• Locate a particular person in an audio-visual document
(information retrieval)• Who is speaking in a meeting ?• Is a suspect the criminal ? (forensic applications)
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Tasks in Automatic Speaker Recognition
• Speaker verification (Voice Biometrics) Are you really who you claim to be ?
• Identification (Speaker ID) : Is this speech segment coming from a known speaker ? How large is the set of speakers (population of the world) ?
• Speaker detection, segmentation, indexing, retrieval, tracking : Looking for recordings of a particular speaker
• Combining Speech and Speaker Recognition Adaptation to a new speaker, speaker typology Personalization in dialogue systems
• Speech data ManagementVoice messaging, Search engines
• Law EnforcementForensics, Home incarceration
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Voice Biometric• Avantages
Often the only modality over the telephone,Low cost (microphone, A/D), UbiquityPossible integration on a smart (SIM) card Natural bimodal fusion : speaking face
• DisadvantagesLack of discretionPossibility of imitation and electronic impostureLack of robustness to noise, distortion,…Temporal drift
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Speaker Identity in Speech• Differences in
Vocal tract shapes and muscular controlFundamental frequency (typical values)