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Using Speaker Recognition

Using Speaker Recognition. Judith A. Markowitz, PhD J. Markowitz, Consultants Chicago, IL www.jmarkowitz.com. AVIOS-Israel Annual Meeting May 15, 2007. What is Speaker Recognition?. A group of biometric technologies that analyze information

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Using Speaker Recognition

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  1. Using Speaker Recognition Judith A. Markowitz, PhD J. Markowitz, Consultants Chicago, IL www.jmarkowitz.com AVIOS-Israel Annual Meeting May 15, 2007

  2. What is Speaker Recognition? A group of biometric technologies that analyze information from the stream of speech

  3. What Speaker Recognition Does • verify a claim of identity (verification) • identify the speaker (identification) • put the speaker into a group/category (classification) • perform other speaker-centric tasks Müller, C., ed. Speaker Classification Volume 4343 & Volume 4441 of Lecture Notes in Computer Science / Artificial Intelligence. Springer, Heidelberg - Berlin - New York (2007).

  4. Speaker Verification • Enrollment • Verification Two-step process

  5. Speaker Verification • Validate enrollee’s identity (authenticity) • Collect samples • Create model (reference model) • Store securely (integrity) Enrollment

  6. Speaker Verification • Claim of identity • User input • Create Model • Retrieve reference model • Matching = One-to-one comparison • Decision= Accept / Reject / Undecided Verification

  7. Claim of Identity

  8. User Input • Password (text dependent) • Challenge-response (text prompted) • Free speech (text independent) Three Main Types

  9. Password

  10. Challenge Response

  11. Multiple AIRS Airplane Cockpit Security • Access to cockpit (SV - text-dependent) • Monitoring of speakers via headsets (SV-text independent) • “Acoustic monitoring” Embedded in cockpit (SI)

  12. Language Independent

  13. Language Dependent

  14. Model Creation & Matching Algorithms • Hidden Markov models • Gaussian classifiers • Neural network • Other

  15. Does SV Work? National Physical Laboratory of the UK Mansfield, Anthony J. & Wayman, James L. 2002 Best Practices in Testing and Reporting Performance of Biometric Devices. Version 2.01. Centre for Mathematics and Scientific Computing Teddington, UK: National Physical Laboratory

  16. Does SV Work? University of Canberra Tests Test 2006* 2005** Persay Persay Nuance Commercial alpha Verifier Counting 1-9 1.05 0.77 0.91 Names 3.74 3.78 5.13 * University of Canberra 2006 PERSAY TECHNOLOGY EVALUATION RESULTS ** University of Canberra 2005 SPEAKER VERIFICATION EVALUATION REPORT Scientific evaluation of speaker verification technologies on behalf of Australian Government Document No: SVE Test Report Version 2.0 Project ID: RFP-SV-026f

  17. Does SV Work? • Mimics • Tape recorders • Noise • Channel & Device Mismatch Challenges- Real and Imagined Most identical to problems facing ASR

  18. Password Reset • Organizations

  19. IVR/Self service • Organizations

  20. Radica Non-IVR Transactions • Organizations

  21. What Is Next? • Multi-factor authentication • Standards • Linguistic elements http://www.voicexml.org/resources/biometrics

  22. Thank you QUESTIONS?

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