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Method for Automatic Online Updating of Personal Biometric Data Based on Speech Signal of the Biometric System User

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The article considers the problem of personal biometric data “aging” over time. A method has been proposed to overcome this problem by automatically updating the specified data in the biometric system storage using the speech signals of registered users obtained during latest requests for their identification and online service. The proposed method uses a scale-invariant indicator of the voice template quality. As a result, it is characterized by guaranteed reliability of the decisions made in the conditions of a wide speech signal dynamic range. It was established that the use of a scale-invariant indicator provides the guaranteed significance level of decisions made by a conventional observer. A full-scale experiment implementing the proposed method has been set up and carried out using an authoring software; practical justification for the effectiveness of the proposed method with real speech data has been given. The results obtained are intended for using in the development of new and modernization of existing systems and technologies for automated quality control and updating of personal biometric data.

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References

  1. S. Kumer, V. K. Lamba, and S. Jangra, “EAgeBioS: Enhanced Biometric System to handle the Effects of Template Aging,” Int. J. Innov. Technol. Explor. Eng., 9, No. 11, 3669–3677 (2019), https://doi.org/10.35940/ijitee.A4756.119119.

    Article  Google Scholar 

  2. I. Manjani, H. Sumerkan, P. J. Flynn, and K. W. Bowyer, “Template aging in 3D and 2D face recognition,” 2016 IEEE 8th Int. Conf. on Biometrics Theory, Applications and Systems (2016), https://doi.org/10.1109/BTAS.2016.7791202.

  3. V. V. Savchenko and A. V. Savchenko, “Method of measuring the acoustic quality indicator of audio recordings prepared for registration and processing in the Unified Biometric System,” Izmer. Tekhn., No. 12, 40–46 (2019), https://doi.org/10.32446/0368-1025it.2019-12-40-46.

  4. V. V. Savchenko and A. V. Savchenko, “Method of real-time updating of voice templates in the Unified Biometric System,” Izmer. Tekhn., No. 5, 58–65 (2020), https://doi.org/10.32446/0368-1025it.2020-5-58-65.

  5. M. Smallman, “Why voice is getting stronger in financial services,” Biometric Technol. Today, 2017, No. 1, 5–7 (2017), https://doi.org/10.1016/S0969-4765(17)30013-9.

    Article  Google Scholar 

  6. N. Crosswhite, J. Byrne, et al., “Template adaptation for face verification and identification,” Image Vision Comput., 79, 35–48 (2018), https://doi.org/10.1016/j.imavis.2018.09.002.

    Article  Google Scholar 

  7. G. Orrù, G. L. Marcialis, and F. Roli, “A novel classification-selection approach for the self updating of template-based face recognition systems,” Pattern Recogn., 100, 107121 (2020), https://doi.org/10.1016/j.patcog.2019.107121.

    Article  Google Scholar 

  8. M. Singh, R. Singh, and A. Ross, “A comprehensive overview of biometric fusion,” Inform. Fusion, 52, No. 12, 187–205 (2019), https://doi.org/10.1016/j.inffus.2018.12.003.

    Article  Google Scholar 

  9. N. N. Lebedeva and E. D. Karimova, “Acoustic characteristics of the speech signal as an indicator of the functional state of a person,” Usp. Fiziol. Nauk, 45, No. 1, 57–95 (2014).

    Google Scholar 

  10. V. V. Savchenko and A. V. Savchenko, “Method for measuring distortion of a speech signal during its transmission over a communication channel to a biometric identification system,” Izmer. Tekhn., No. 11, 65–72 (2020), https://doi.org/10.32446/0368-1025it.2020-11-65-72.

  11. A. V. Savchenko, V. V. Savchenko, and L. V. Savchenko, “Optimization of Gain in Symmetrized Itakura-Saito Discrimination for Pronunciation Learning,” in: Mathematical Optimization Theory and Operations Research, Springer, Cham (2020), pp. 440–454, https://doi.org/10.1007/978-3-030-49988-4_30.

  12. S. Kullback, Information Theory and Statistics, Dover Publ., New York (1997).

    MATH  Google Scholar 

  13. V. V. Savchenko, “Itakura-Saito divergence as an element of the information theory of speech perception,” J. Commun. Technol. El., 64, No. 6, 590–596 (2019), https://doi.org/10.1134/S1064226919060093.

    Article  Google Scholar 

  14. V. V. Savchenko and L. V. Savchenko, “Method for measuring the speech signal intelligibility indicator in the Kullback–Leibler information metric,” Izmer. Tekhn., No. 9, 59–64 (2019), https://doi.org/10.32446/0368-1025it.2019-9-59-64.

  15. V. V. Savchenko and A. V. Savchenko, “Guaranteed significance level criterion in automatic speech signal segmentation,” J. Commun. Technol. El., 65, No. 11, 1311–1317 (2020), https://doi.org/10.1134/S1064226920110157.

    Article  Google Scholar 

  16. H. B. Kashani, A. Sayadiyan, and H. Sheikhzadeh, “Vowel detection using a perceptually-enhanced spectrum matching conditioned to phonetic context and speaker identity,” Speech Commun., 91, 28–48 (2017), https://doi.org/10.1016/j.specom.2017.04.008.

    Article  Google Scholar 

  17. A. V. Savchenko, “Efficient facial representations for age, gender and identity recognition in organizing photo albums using multi-output ConvNet,” PeerJ Comput. Sc., 5e, 197 (2019), https://doi.org/10.7717/peerj-cs.197.

    Article  Google Scholar 

  18. A. A. Borovkov, Mathematical Statistics, Lan’, St. Petersburg (2010).

    Google Scholar 

  19. Z. Meng, M. U. B. Altaf, and B. H. F. Juang, “Active voice authentication,” Digit. Signal Process., 101, 102672 (2020), https://doi.org/10.1016/j.dsp.2020.102672.

    Article  Google Scholar 

  20. S. L. Marple, Digital Spectral Analysis with Applications, Dover Publ., Mineola, New York (2019), 2nd ed., https://www.goodreads.com/book/show/19484239.

  21. P. H. Müller, P. Neumann, and R. Storm, Tables for Mathematical Statistics, VEB Fachbuchverlag, Leipzig (1973), https://doi.org/10.1002/bimj.19740160816.

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Authors and Affiliations

  1. National Research University Higher School of Economics, Nizhny Novgorod, Russia

    A. V. Savchenko

  2. Nizhny Novgorod State Linguistic University, Nizhny Novgorod, Russia

    V. V. Savchenko

Authors
  1. A. V. Savchenko
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  2. V. V. Savchenko
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Correspondence to A. V. Savchenko.

Additional information

Translated from Izmeritel’naya Tekhnika, No. 11, pp. 60–66, November, 2021.

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Savchenko, A.V., Savchenko, V.V. Method for Automatic Online Updating of Personal Biometric Data Based on Speech Signal of the Biometric System User. Meas Tech 64, 928–935 (2022). https://doi.org/10.1007/s11018-022-02025-4

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  • DOI: https://doi.org/10.1007/s11018-022-02025-4

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