Advertisement

U-NORM Likelihood Normalization in PIN-Based Speaker Verification Systems

  • D. Garcia-Romero
  • J. Gonzalez-Rodriguez
  • J. Fierrez-Aguilar
  • J. Ortega-Garcia
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2688)

Abstract

This paper present a new likelihood normalization technique, entitled U-NORM, for speaker recognition systems based on short utterances. A comparison between this new approach and the widely used Z-NORM is reported and evaluated. Phonetic dependency between the speaker model and the test speech utterances is determined as the main impediment for a good performance of Z-NORM technique. A set of experiments are developed on a specifically acquired PIN-oriented real-users database showing the higher performance of the new technique for PIN based security applications. U-NORM provides a common likelihood scale for all system users allowing speaker independent thresholds that simplify the enrollment process and add robustness to PIN based security applications.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [2]
    Douglas A. Reynolds et al., “Speaker Verification using Adapted Gaussian Mixture Models, Digital Signal Processing”, vol. 10, pp. 19–41 (2000).Google Scholar
  2. [3]
    J.-B. Pierrot et al., “A Comparison of a Priori Threshold setting Procedures for Sperker Verification in the CAVE Project”, ICASSP’98.Google Scholar
  3. [4]
    D. García-Romero et al., “ATVS-UPM Results and Presentation at NIST’2002 Speaker Recognition Evaluation”, Vienna, VA, 2002.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • D. Garcia-Romero
    • 1
  • J. Gonzalez-Rodriguez
    • 1
  • J. Fierrez-Aguilar
    • 1
  • J. Ortega-Garcia
    • 1
  1. 1.Speech and Signal Processing Group (ATVS)Universidad Politécnica de Madrid (UPM)Spain

Personalised recommendations