The problem of automated quality control of audio recordings containing voice samples of individuals is considered. It is shown that in solving this problem the most acute impediment is the problem of small samples of observations. To overcome the problem, a new, high-speed method of acoustic measurements is proposed, based on the principle of relative stability of the frequency of the fundamental tone of a speech signal within a voice sample of short duration. An example of practical implementation of the developed method according to a scheme with inter-period signal accumulation is considered. Using proprietary software, a full-scale experiment was conducted in which statistical estimates of the effectiveness of the method in noise conditions were obtained. It is shown that when using the proposed method, if the signal-to-noise ratio is lower than 15 dB, an audio recording is rejected with a probability of 0.95 or more as unsuitable for biometric identification of a person. The results obtained are intended for use in the development of new systems and modernization of existing systems and technologies for collection and automated quality control of biometric personal data. The article is intended for a wide circle of specialists in the field of acoustic measurements and digital processing of speech signals, as well as for practitioners who organize the work of authorized organizations in preparing samples of biometric personal data for registration with the unified biometric system (UBS).
Similar content being viewed by others
References
V. V. Savchenko, J. Commun. Technol. Electron., 61, No. 12, 1374–1379 (2016), DOI: https://doi.org/10.1134/S1064226916120226.
M. Kunesova, M. Hruz, Z. Zajíc, and V. Radova, Proc. 21st Int. Conf. on Speech and Computer SPECOM-2019, Istanbul, Turkey, Aug. 20–25, 2019, Springer, Istanbul (2019), pp. 247–257, DOI: https://doi.org/10.1007/978-3-030-26061-3_26.
A. Joshi, M. Kumar M., and P. K. Das, Proc. 2016-Int. Conf. on Signal Processing and Communication (ICSC), Noida, India , IEEE Conf. Publ. (2017), pp. 191–196, DOI: https://doi.org/10.1109/ICSPCom.2016.7980574.
N. Prodi and Ch. Visentin, Appl. Acoust., 152, 63–72 (2019), DOI: https://doi.org/10.1016/j.apacoust.2019.03.026.
B. Desplanques, K. Demuynck, and J. P. Martens, Comp. Speech & Lang., 46, No. 5, 72–93 (2017), DOI: https://doi.org/10.1016/j.csl.2017.05.05.003.
V. V. Savchenko, Radiophys. Quant. Electron., 60, No. 1, 89–96 (2017), DOI: https://doi.org/10.1007/s11141-017-9778-y.
A. V. Savchenko and V. V. Savchenko, “Method for measuring the fundamental frequency for acoustic speech analysis systems,” Izmeri. Tekhn., No. 3, 59–63 (2019), DOI: https://doi.org/10.32446/0368-1025it.2019-3-59-63.
V. V. Savchenko, “A new concept of software for statistical processing of information based on the prognostic function of probability theory,” Nauch. Ved. Belgor. Gos. Univ., Ser. Ekon., Inform., No. 7 (204), Iss. 34/1, 84–88 (2015).
Voice Self-Analysis V2 Software, https://sites.google.com/site/frompldcreators/VoiceSelfAnalysisV2.zip, acces. Sept.20, 2019.
M. G. Christensen, Introduction to Audio Processing, Springer, Cham (2019), DOI: https://doi.org/10.1007/978-3-030-11781-8.
L. Sukhostat and Ya. Imamverdiyev, J. Voice, 29, No. 4, 410–417 (2015), DOI: https://doi.org/10.1016/j.jvoice.2014.09.016.
V. S. Parshin and V. S. Gusev, “Influence of noise interference on the accuracy of estimating the center frequency of the spectrum of a narrow-band signal,” Izmer. Tekhn., No. 7, 56–59 (2005).
J. Zeremdini, M. A. Ben Messaoud, and A. Bouzid, Appl. Acoust., 120, No. 1, 45–53 (2017), DOI: https://doi.org/10.1016/j.apacoust.2017.01.013.
Protocol of Field Tests, https://sites.google.com/site/frompldcreators/protocol_3-1.pdf, acces. 09.20.2019.
M. A. Hasan, Int. J. Comp. Sci. Netw. Secur., 17, No. 2, 106–112 (2017).
J. Stahla and P. Mowlaee, Speech Commun., 111, No. 8, 1–13 (2019), DOI: https://doi.org/10.1016/j.specom.2019.05.001.
I. C. Yadav, S. Shahnawazuddin, and G. Pradhan, Dig. Signal Proces., 86, No. 12, 55–64 (2019), DOI: https://doi.org/10.1016/j.dsp.2018.12.01.01.
V. V. Savchenko, J. Communic. Technol. Electron., 63, No. 1, 53–57 (2018), DOI: https://doi.org/10.1134/S1064226918010126.
L. Arnaud, V. Gracco, and L. Menard L., Neuropsychologia, 117, No. 6, 261–270 (2018), DOI: https://doi.org/10.1016/j.neuropsychologia.2018.06.009.
R. Sinha and S. Shahnawazuddin, Comp. Speech & Lang., 48, No. 10, 103–121 (2018), DOI: https://doi.org/10.1016/j.csl.2017.10.007.
G. M. Bidelman and A. Yellamsetty, Hearing Res., 351, No. 5, 34–44 (2017), DOI: https://doi.org/10.1016/j.heares.2017.05.05.008.
Author information
Authors and Affiliations
Corresponding author
Additional information
Translated from Izmeritel’naya Tekhnika, No. 12, pp. 40–46, December, 2019.
Rights and permissions
About this article
Cite this article
Savchenko, V.V., Savchenko, A.V. Method for Measuring the Indicator of Acoustic Quality of Audio Recordings Prepared for Registration and Processing in the Unified Biometric System. Meas Tech 62, 1071–1078 (2020). https://doi.org/10.1007/s11018-020-01736-w
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11018-020-01736-w