Introduction

Chapter
Part of the Signals and Communication Technology book series (SCT)

Abstract

Authentication is the process of positively verifying the identity of a user, device, or any entity in a computer system, often as a prerequisite to allowing access to resources in the system. Authentication has been used by human for thousands of years to recognize each other, to identify friends and enemies, and to protect their information and assets. In the computer era, the purpose of identification is more than just to identify people in our presence, but also to identify people in remote locations, computers on a network, or any entity in computer networks. As such, authentication has been extended from a manual identification process to an automatic one. People are now paying more and more attention to security and privacy; thus authentication processes are everywhere in our daily life. Automatic authentication technology is now necessary for all computer and network access and it plays an important role in security.

Keywords

Speaker Recognition Discriminative Training Digit String Speaker Recognition System Automatic Speaker 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Atal, B. S.: Automatic recognition of speakers from their voices. Proceedings of the IEEE 64, 460–475 (1976)CrossRefGoogle Scholar
  2. 2.
    Atal, B. S.: "Effectiveness of linear prediction characteristics of the speech wave for automatic speaker identification and verification". Journal of Acoustical Society of America 55, 1304–1312 (1974)CrossRefGoogle Scholar
  3. 3.
    Atal, B.: Automatic speaker recognition based on pitch contours. PhD thesis, Polytech. Inst., Brookly, NY, June 1968Google Scholar
  4. 4.
    Campbell, J. P.: Forensic speaker recognition. IEEE Signal Processing Magazine 95–103, March 2009Google Scholar
  5. 5.
    Campbell, J. P.: "Speaker recognition: A tutorial". Proceedings of the IEEE 85, 1437–1462 (1997)CrossRefGoogle Scholar
  6. 6.
    Campbell, W. M.: "Support vector machines using GMM supervectors for speaker verification". IEEE Signal Processing Letter, pp. 308–311, May 2006Google Scholar
  7. 7.
    Davis, S. B., Mermelstein, P.: “Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences”. IEEE Trans. on Acoustics, speech, and signal processing ASSP-28, 357–366 (1980)CrossRefGoogle Scholar
  8. 8.
    Doddington, G.: “Speaker recognition—identifying people by their voices”. Proceedings of the IEEE 73, 1651–1664 (1985)CrossRefGoogle Scholar
  9. 9.
    Doddington G. “Speaker verification—final report”. Tech Rep. RADC 74–179, Rome Air Development Center, Griffiss AFB, NY, Apr. (1974)Google Scholar
  10. 10.
    FpVTE2003, “ http://www.nist.gov/itl/iad/ig/fpvte03.cfm,” in Proceedings of The Fingerprint Vendor Technology Evaluation (FpVTE), 2003
  11. 11.
    Furui, S.: “Cepstral analysis techniques for automatic speaker verification”. IEEE Trans. Acoust., Speech, Signal Processing 27, 254–277 (1981)CrossRefGoogle Scholar
  12. 12.
    FVC2004, “http://bias.csr.unibo.it/fvc2004/,” in Proceedings of The Third International Fingerprint Verification Competition, 2004
  13. 13.
    FVC2006, “http://bias.csr.unibo.it/fvc2006/,” in Proceedings of The Fourth International Fingerprint Verification Competition, 2006
  14. 14.
    Gish H., Schmidt M. “Text-independent speaker identification.” IEEE Signal Processing Magazine, pp.18–32, Oct. 1994Google Scholar
  15. 15.
    Jain, A. K., Ross, A., Prabhakar, S.: “An introduction to biometric recognition”. IEEE Trans. on Circuits and System for Video Tech. 14, 4–20 (2004)CrossRefGoogle Scholar
  16. 16.
    Kenny P., Dumouchel P. “Experiments in speaker verification using factor analysis likelihood ratios.” in Proceedings of Odyssey, pp. 219–226 (2004)Google Scholar
  17. 17.
    Li, K., Hughes, G.: “Talker differences as they appear in correlation matrices of continuous speech spectra”. J. Acoust. Soc. Amer. 55, 833–837 (1974)CrossRefGoogle Scholar
  18. 18.
    Li K., and J.E. Dammann , W. C., “Experimental studies in speaker verification using an adaptive system". J. Acoust. Soc. Amer., vol. 40, pp. 966–978, Nov. 1966Google Scholar
  19. 19.
    Li Q. “An auditory-based transform for audio signal processing.” in Proceedings of IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (New Paltz, NY), Oct. 2009Google Scholar
  20. 20.
    Li Q., Huang Y. “An auditory-based feature extraction algorithm for robust speaker identification under mismatched conditions,” IEEE Trans. on Audio, Speech and Language Processing, Sept. 2011Google Scholar
  21. 21.
    Li Q., Huang Y. “Robust speaker identification using an auditory-based feature,” in ICASSP 2010 (2010)Google Scholar
  22. 22.
    Li Q., Juang B.-H. “Speaker verification using verbal information verification for automatic enrollment.” in Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (Seattle), May 1998Google Scholar
  23. 23.
    Li, Q., Juang, B.-H., Lee, C.-H., Zhou, Q., Soong, F. K.: “Recent advancements in automatic speaker authentication”. IEEE Robotics & Automation magazine 6, 24–34 (1999)CrossRefGoogle Scholar
  24. 24.
    Li, Q., Juang, B.-H., Zhou, , Q., , Lee, C.-H.: “Automatic verbal information verification for user authentication”. IEEE Trans. on Speech and Audio Processing 8, 585–596 (2000)CrossRefGoogle Scholar
  25. 25.
    Li Q., Juang B.-H., Zhou Q., Lee C.-H. “Verbal information verification.” in Proceedings of EUROSPEECH (Rhode, Greece), pp. 839–842, Sept. 22–25 (1997)Google Scholar
  26. 26.
    Li Q., Parthasarathy S., Rosenberg A. E. “A fast algorithm for stochastic matching with application to robust speaker verification.” in Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (Munich), pp. 1543–1547, April (1997)Google Scholar
  27. 27.
    Liu C.-S., Lee C.-H., Juang, B.-H., Rosenberg A. “Speaker recognition based on minimum error discriminative training,” in Proc. IEEE Int. Conf. Acoust., Speech, Signal Processing, 1994Google Scholar
  28. 28.
    Martin A., Doddington G., Kamm T., Ordowski M., Przybocki M., “The DET curve in assessment of detection ask performance,” in Proceedings of Eurospeech (Rhodes, Greece), pp. 1899–1903, Sept. (1997)Google Scholar
  29. 29.
    Matsui, T., Furui, S.: “Comparison of text-independent speaker recognition methods using VQ-distoration and discrete/continuous HMM’s". IEEE Trans. on speech and Audio Processing 2, 456–459 (1994)CrossRefGoogle Scholar
  30. 30.
    Monrose F., Reiter M. K., Li Q., Wetzel S. “Using voice to generate cryptographic keys: a position paper.” in Proceedings of Speaker Odyssey, June (2001)Google Scholar
  31. 31.
    Monrose F., Reiter M. K., Q. Li D. L., and Shih, C., “Toward speech generated cryptographic keys on resource constrained devices,” in Proceedings of the 11th USENIX Security Symposium, August (2002)Google Scholar
  32. 32.
    O’Gorman, L.: “Comparing passwords, tokens, and biometrics for user authentication”. Proceedings of the IEEE 91, 2021–2040 (2003)CrossRefGoogle Scholar
  33. 33.
    Parthasarathy S., Rosenberg A. E. “General phrase speaker verification using sub-word background models and likelihood-ratio scoring,” in Proceedings of ICSLP-96, (Philadelphia), October 1996Google Scholar
  34. 34.
    Phillips P. J., “Mbgc portal challenge version 2 preliminary results.” in Proceedings of MBGC Third Workshop, (2009)Google Scholar
  35. 35.
    Phillips P. J., Scruggs W. T., O’Toole A. J., Flynn P. J., Bowyer K. W., Schott C. L., Sharpe M. “Frvt 2006 and ice 2006 large-scale results.” in NISTIR, March (2007)Google Scholar
  36. 36.
    Pruzansky, S.: “Pattern-matching procedure for automatic talker recognition”. J. Acoust. Soc. Amer. 35, 354–358 (1963)CrossRefGoogle Scholar
  37. 37.
    Przybocki, M., Martin, A., Le, A.: “NIST speaker recognition evaluations utilizing the mixer corpora—2004, 2005, 2006”. IEEE Trans. Audio, Speech and Language Processing 15, 1951–1959 (2007)CrossRefGoogle Scholar
  38. 38.
    Reynolds, D., Rose, R. C.: “Robust text-independent speaker identification using Gaussian mixture speaker models”. IEEE Trans. on Speech and Audio Processing 3, 72–83 (1995)CrossRefGoogle Scholar
  39. 39.
    Rosenberg, A. E.: “Automatic speaker verification: a review”. Proceedings of the IEEE 64, 475–487 (1976)CrossRefGoogle Scholar
  40. 40.
    Rosenberg A. E., DeLong J. “HMM-based speaker verification using a telephone network database of connected digital utterances.” Technical Memorandum BL01126-931206-23TM, AT&T Bell Laboratories, December 1993Google Scholar
  41. 41.
    Rosenberg A. E., DeLong J., Lee C.-H., Juang B.-H., Soong, F. K. “The use of cohort normalized scores for speaker verification,” in Proceedings of the International Conference on Spoken Language Processing (Banff, Alberta, Canada), pp. 599–602, October (1992)Google Scholar
  42. 42.
    Rosenberg A. E., Lee C.-H., Juang B.-H. “Subword unit talker verification using hidden markov models.” in Proc. IEEE Int. Conf. Acoust., Speech, Signal Processing, pp. 269–272 (1990)Google Scholar
  43. 43.
    Rosenberg A. E., Parthasarathy S. “Speaker background models for connected digit password speaker verification.” in Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (Atlanta), pp. 81–84, May (1996)Google Scholar
  44. 44.
    Rosenberg A., Soong F. “Recent research in automatic speaker recognition.” in Advances in Speech Signal Processing (Furui, S. and Sondhi, M., eds.), pp. 701–738, NY: Marcel Dekker (1992)Google Scholar
  45. 45.
    Savic M. Gupta S. “Variable parameter speaker verification system based on hidden Markov modeling.” in Proc. IEEE Int. Conf. Acoust., Speech, Signal Processing, pp. 281–284, (1990)Google Scholar
  46. 46.
    Schmidt M., Gish H. “Speaker identification via support vector machine,” in Proc. IEEE Int. Conf. Acoust., Speech, Signal Processing, pp. 105–108 (1996)Google Scholar
  47. 47.
    Simmons, G. J.: “A survey of information authentication”. Proceedings of the IEEE 76, 603–620 (1988)CrossRefGoogle Scholar
  48. 48.
    Soong, F. K., Rosenberg, A. E.: “On the use of instantaneous and transitional spectral information in speaker recognition”. IEEE Tran. Acoust., Speech, Signal Processing ASSP-36, 871–879 (1988)CrossRefGoogle Scholar
  49. 49.
    Stocksdale G. “Glossary of terms used in security and intrusion detection.” Online, NSA, 2009.http://www.sans.org/newlook/resources/glossary.ht
  50. 50.
    Tishby N. “Information theoretic factorization of speaker and language in hidden markov models, with application to speaker recognition.” in Proc. IEEE Int. Conf. Acoust., Speech, Signal Processing, pp. v1:97–90 (1988) Google Scholar
  51. 51.
    Woodward J. D., Webb K. W., Newton E. M., Bradley M. A., Rubenson D., Larson K., Lilly J., Smythe K., Houghton B., Pincus H. A., Schachter J., Steinberg P. Army Biometric Applications Identifying and Addressing Sociocultural Concerns. RAND Arrayo (2001)Google Scholar
  52. 52.
    Yin Y., Li Q. “Soft frame margin estimation of Gaussian mixture models for speaker recognition with sparse training data.” in ICASSP 2011 (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg  2012

Authors and Affiliations

  1. 1.Li Creative Technologies (LcT), IncFlorham ParkUSA

Personalised recommendations