Effective speaker verification via dynamic mismatch compensation

Effective speaker verification via dynamic mismatch compensation

For access to this article, please select a purchase option:

Buy article PDF
(plus tax if applicable)
Buy Knowledge Pack
10 articles for $120.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Your details
Why are you recommending this title?
Select reason:
IET Biometrics — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

This paper presents a new approach to condition-adjusted T-norm (CT-Norm) for speaker verification under significant mismatched noise conditions. The study is motivated by the fact that, though the standard CT-Norm method offers enhanced accuracy under mismatched data conditions, its effectiveness reduces with the increased severity of such conditions. The proposed approach attempts to address this challenge by providing a more effective reduction of data mismatch through the incorporation of multi-signal-to-noise ratio (SNR) universal background models (UBMs). The effectiveness of the proposed approach is demonstrated through experiments based on examples of real-world noise. It is shown that the superiority of the approach over CT-Norm is particularly significant for such excessive levels of test data degradation considered in the study as 5 dB SNR and below. The paper provides a description of the characteristics of the proposed approach and details the experimental analysis of its effectiveness under different noise conditions.


    1. 1)
    2. 2)
    3. 3)
    4. 4)
    5. 5)
      • Ariyaeeinia, A.M., Sivakumaran, P.: `Analysis and comparison of score normalisation methods for text-dependent speaker verification', Proc. Eurospeech'97, 1997, Rhodes, Greece, 3, p. 1379–1382.
    6. 6)
    7. 7)
      • Sturim, D.E., Reynolds, D.A.: `Speaker adaptive cohort selection for T-norm in text-independent speaker verification', Proc. Int. Conf. on Acoustics, Speech and Signal Process (ICASSP), 2005, 1, p. 741–744.
    8. 8)
      • S.G. Pillay , A. Ariyaeeinia , M. Pawlewski , P. Sivakumaran . ‘Speaker verification under mismatched data conditions’, IET signal processing.
    9. 9)
      • Yang, L., Gong, W.: `Multi-SNR GMMs-based noise-robust speaker verification using 1/f', Proc. Int. Conf. on Pattern Recognition (ICPR'06), 2006, p. 241–244.
    10. 10)
      • J.H. Garofolo , L.F. Lamel , W.M. Fisher . (1993) TIMIT acoustic-phonetic continuous speech corpus.
    11. 11)
    12. 12)
      • McCowan, I., Pelecanos, J., Sridharan, S.: `Robust speaker recognition using microphone arrays', Proc. Speaker Odyssey’01, 2001, Crete, Greece, p. 101–106.
    13. 13)
    14. 14)
      • A. Varga , H.J.M. Steeneken , M. Tornlinson , D. Jones . (1992) The NOISEX-92 study on the effect of additive noise on automatic speech recognition.
    15. 15)
      • Sivakumaran, P.: `Robust text-dependent speaker verification', 1998, PhD, University of Hertfordshire, School of Electronic, Communication and Electrical Eng., Herts, UK.
    16. 16)
      • Bellot, O., Matrouf, D., Merlin, T., Bonastre, J.F.: `Additive and convolutional noises compensation in speaker recognition', Proc. Int. Conf. on Spoken Language Processing (ICSLP’00), 2000, Beijing, China, 2, p. 799–802.
    17. 17)
      • `Large scale evaluation of automatic speaker verification technology', Dialogues Spotlight Technology Report, CCIR, 2000.
    18. 18)
      • Fortuna, J., Sivakumaran, P., Ariyaeeinia, A., Malegaonkar, A.: `Relative effectiveness of score normalization methods in open-set speaker identification', Proc. IEEE Speaker and Language Recognition Workshop (Odyssey’04), 2004, p. 369–376.

Related content

This is a required field
Please enter a valid email address