Skip to main content

Optimization of Usability on an Authentication System Built from Voice and Neural Networks

  • Conference paper
  • 1051 Accesses

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3045))

Abstract

While multilayer perceptrons (MLPs) have great possibility on the application to speaker verification, they suffer from an inferior learning speed. To appeal to users, the speaker verification systems based on MLPs must achieve a reasonable speed of user enrolling and it is thoroughly dependent on fast learning of MLPs. To attain real-time enrollment for the systems, the previous two studies, the discriminative cohort speakers (DCS) method and the omitting patterns in instant learning (OIL) method, have been devoted to the problem and each satisfied that objective. In this paper, we combine the two methods and apply the combination to the systems, assuming that the two methods operate on different optimization principles. Through experiment on real speech database using an MLP-based speaker verification system to which the combination is applied, the feasibility of the combination is verified from the results. Keywords: Biometric authentication system, speaker verification, multiplayer perceptrons, error backpropagation, real-time enrollment, discriminative cohort speakers, omitting patterns in instant learning

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Matsui, T., Aikawa, K.: Robust Model for Speaker Verification against Session-Dependent Utterance Variation. IEEE International Conference on Acoustics, Speech and Signal Processing 1, 117–120 (1998)

    Google Scholar 

  2. Mistretta, W., Farrell, K.: Model Adaptation Methods for Speaker Verification. IEEE International Conference on Acoustics, Speech and Signal Processing 1, 113–116 (1998)

    Google Scholar 

  3. Matsui, T., Furui, S.: Speaker Adaptation of Tied-Mixture-Based Phoneme Models for Text-Prompted Speaker Recognition. IEEE International Conference on Acoustics, Speech and Signal Processing 1, 125–128 (1994)

    Google Scholar 

  4. Rosenberg, A.E., Parthasarathy, S.: Speaker Background Models for Connected Digit Password Speaker Verification. IEEE International Conference on Acoustics, Speech, and Signal Processing 1, 81–84 (1996)

    Google Scholar 

  5. Bengio, Y.: Neural Networks for Speech and Sequence Recognition. International Thomson Computer Press, London (1995)

    Google Scholar 

  6. Lee, T., Choi, H., Kwag, Y., Hwang, B.: A Method on Improvement of the Online Mode Error Backpropagation Algorithm for Pattern Recognition. In: Ishizuka, M., Sattar, A. (eds.) PRICAI 2002. LNCS (LNAI), vol. 2417, pp. 275–284. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  7. Lawrence, S., Giles, C.L.: Overfitting and Neural Networks: Conjugate Gradient and Backpropagation. In: IEEE-INNS-ENNS International Joint Conference on Neural Networks, vol. 1, pp. 114–119 (2000)

    Google Scholar 

  8. LeCun, Y.: Generalization and Network Design Strategies. Department of Computer Science, University of Toronto (1989)

    Google Scholar 

  9. Lee, T., Choi, S., Choi, W., Park, H., Lim, S., Hwang, B.: Faster Speaker Enrollment for Speaker Verification Systems Based on MLPs by Using Discriminative Cohort Speakers Method. In: Chung, P.W.H., Hinde, C.J., Ali, M. (eds.) IEA/AIE 2003. LNCS (LNAI), vol. 2718, pp. 734–743. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  10. Lee, T., Choi, S., Choi, W., Park, H., Lim, S., Hwang, B.: A Qualitative Discriminative Cohort Speakers Method to Reduce Learning Data for MLP-Based Speaker Verification Systems. In: Liu, J., Cheung, Y.-m., Yin, H. (eds.) IDEAL 2003. LNCS, vol. 2690, pp. 1082–1086. Springer, Heidelberg (2003)

    Google Scholar 

  11. Becchetti, C., Ricotti, L.P.: Speech Recognition: Theory and C++ Implementation. John Wiley & Sons, Chinchester (1999)

    Google Scholar 

  12. Cristea, P., Valsan, Z.: New Cepstrum Frequency Scale for Neural Network Speaker Verification. IEEE International Conference on Electronics, Circuits and Systems 3, 1573–1576 (1999)

    Google Scholar 

  13. Savic, M., Sorensen, J.: Phoneme Based Speaker Verification. IEEE International Conference on Acoustics, Speech, and Signal Processing 2, 165–168 (1992)

    Google Scholar 

  14. Delacretaz, D.P., Hennebert, J.: Text-Prompted Speaker Verification Experiments with Phoneme Specific MLPs. IEEE International Conference on Acoustics, Speech, and Signal Processing 2, 777–780 (1998)

    Google Scholar 

  15. Lippmann, R.P.: An Introduction to Computing with Neural Nets. IEEE Acoustics, Speech, and Signal Processing Magazine 4, 4–22 (1987)

    Google Scholar 

  16. Lee, T., Hwang, B.: Continuants Based Neural Speaker Verification System (2004); To be published in Lecture Notes in Artificial Intelligence

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2004 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Lee, TS., Hwang, BW. (2004). Optimization of Usability on an Authentication System Built from Voice and Neural Networks. In: Laganá, A., Gavrilova, M.L., Kumar, V., Mun, Y., Tan, C.J.K., Gervasi, O. (eds) Computational Science and Its Applications – ICCSA 2004. ICCSA 2004. Lecture Notes in Computer Science, vol 3045. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24767-8_40

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-24767-8_40

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22057-2

  • Online ISBN: 978-3-540-24767-8

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics