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Method for measuring distortions of a speech signal during its transmission over a communication channel to a biometric identification system
https://doi.org/10.32446/0368-1025it.2020-11-65-72
Abstract
This paper is devoted to the presence of distortions in a speech signal transmitted over a communication channel to a biometric system during voice-based remote identification. We propose to preliminary correct the frequency spectrum of the received signal based on the pre-distortion principle. Taking into account a priori uncertainty, a new information indicator of speech signal distortions and a method for measuring it in conditions of small samples of observations are proposed. An example of fast practical implementation of the method based on a parametric spectral analysis algorithm is considered. Experimental results of our approach are provided for three different versions of communication channel. It is shown that the usage of the proposed method makes it possible to transform the initially distorted speech signal into compliance on the registered voice template by using acceptable information discrimination criterion. It is demonstrated that our approach may be used in existing biometric systems and technologies of speaker identification.
About the Authors
V. V. Savchenko
Nizhniy Novgorod State Linguistic University
Russian Federation
Russian Federation
Vladimir V. Savchenko
Nizhniy Novgorod
A. V. Savchenko
HSE University, Laboratory of algorithms and technologies for network analysis
Russian Federation
Russian Federation
Andrey V. Savchenko
Nizhniy Novgorod
References
1. Dargan S., Kumar M., Expert Systems with Applications, 2020, vol. 143, no. 1, рр. 113–114. https://doi.org/10.1016/j.eswa.2019.113114
2. Sholokhov A., Kinnunenc T., Vestman V., Lee K. A., Computer Speech & Language, 2020, vol. 60, no. 3, р. 101024. https://doi.org/10.1016/j.csl.2019.101024
3. Savchenko V. V., Savchenko A. V., Measurement Techniques, 2020, vol. 63, no. 5, рр. 391–400. https://doi.org/10.1007/s11018-020-01800-5
4. Savchenko V. V., Radioelectronics and Communications Systems, 2020, vol. 63, no. 1, рр. 42–54. https://doi.org/10.3103/S0735272720010045
5. Rakhmanenko I. A., Meshcheryakov R. V., Proceedings of the SPIIRAN, 2017, vol. 52, no. 3, pp. 32–50. https://doi.org/10.15622/sp.52.2
6. Spravochnik po radioelektronnym ustroystvam: in 2 vol., vol. 2, ed. D. P. Linde, Moscow, Energiya Publ., 1978, 328 p. (in Russian).
7. Kenny P., Boulianne G., Ouellet P., Dumouchel P., IEEE Transactions on Audio, Speech and Language Processing, 2007, vol. 15, no. 4, рр. 1435–1447. https://doi.org/10.1109/TASL.2006.881693
8. Motlicek P. et al., IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2015, рр. 4445–4449. https://doi.org/10.1109/ICASSP.2015.7178811
9. Yang B., Fu X., Sidiropoulos N. D., IEEE Transactions on Signal Processing, 2016, vol. 65, no. 5, рр. 256–269. https://doi.org/10.1109/TSP.2016.2614491
10. Farooq M., Adeeba F., Hussain S., The 22nd Conference of the Oriental COCOSDA, Cebu City, Philippines, October 25–27, 2019. https://doi.org/10.1109/O-COCOSDA46868.2019.9041237
11. Galyashina Ye. I., Proceedings of the XXIII International Conference, in 2 vol., Irkutsk, Vostochno-Sibirskiy institut MVD RF Publ., 2018.
12. Liu D., He, Z., Chen D., IEEE Transactions on Neural Networks and Learning Systems, 2019, no. 12, рр. 1–14. https://doi.org/10.1109/TNNLS.2019.2951803
13. Barra S., Castiglione A., Narducci F., De Marsico M., Nappi M., Image and Vision Computing, 2018, vol. 76, no. 8, рр. 27–37. https://doi.org/10.1016/j.imavis.2018.05.001
14. Mahé G., Gilloire A., Gros L., Speech Communication, 2004, vol. 43, no. 8, рр. 241–266. https://doi.org/10.1016/j.specom.2004.06.002
15. Xie F. L., Soong F. K., Lia H. A., Speech Communication, 2019, vol. 106, no. 12, рр. 57–67. https://doi.org/10.1016/j.specom.2018.11.007
16. Park H. J., Ha I. H., Han S. K., Optics Communications, 2019, vol. 444, no. 8, рр. 160–164. https://doi.org/10.1016/j.optcom.2019.03.075
17. Anzar S. M., Amala K., Rajendran R., Computers & Electrical Engineering, 2016, vol. 50, no. 2, рр. 10–25. https://doi.org/10.1016/j.compeleceng.2015.12.003
18. Savchenko V. V., Savchenko А. V., Radioelectronics and Communications Systems, 2019, vol. 62, no. 5, рр. 276–286. https://doi.org/10.3103/S0735272719050042
19. Savchenko V. V., Radiophysics and Quantum Electronics, 2015, vol. 58, no. 5, рр. 373–379. https://doi.org/10.1007/s11141-015-9611-4
20. Savchenko V. V., Journal of Communications Technology and Electronics, 2019, vol. 64, no. 6, рр. 590–596. https://doi.org/10.1134/S1064226919060093
21. Savchenko V. V., Savchenko L. V., Measurement Techniques, 2019, vol. 62, no. 9, pp. 832–839. https://doi.org/10.1007/s11018-019-01702-1
Review
For citations:
Savchenko V.V., Savchenko A.V. Method for measuring distortions of a speech signal during its transmission over a communication channel to a biometric identification system. Izmeritel`naya Tekhnika. 2020;(11):65-72. (In Russ.) https://doi.org/10.32446/0368-1025it.2020-11-65-72
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