Architecture and Operation Algorithm of a Biometric Voice Authentication System
Author:
Publisher
Allerton Press
Subject
Signal Processing,Control and Systems Engineering,Software
Link
https://link.springer.com/content/pdf/10.3103/S014641162108006X.pdf
Reference10 articles.
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2. Zegzhda, P., Zegzhda, D., Pavlenko, E., and Ignatev, G., Applying deep learning techniques for Android malware detection, Proc. 11th Int. Conf. on Security of Information and Networks, Cardiff, UK, 2018, New York: Association for Computing Machinery, 2018, p. 7. https://doi.org/10.1145/3264437.3264476
3. Zegzhda, P.D., Malyshev, E.V., and Pavlenko, E.Yu., The use of an artificial neural network to detect automatically managed accounts in social networks, Autom. Control Comput. Sci., 2017, vol. 51, no. 8, pp. 874–880. https://doi.org/10.3103/S0146411617080296
4. Belova, Ye.P. and Mashkina, I.V., Research results of artificial neural network for user authentication according to frequency of fourth formant of vowel sound phoneme, in Int. Russian Automation Conf. (RusAutoCon), Sochi, Russia, 2018, IEEE, 2018, pp. 1–6. https://doi.org/10.1109/RUSAUTOCON.2018.8501680
5. Rabiner, L.R. and Shafer, R.V., Tsifrovaya obrabotka rechevykh signalov (Digital Processing of Speech Signals), Moscow: Radio i Svyaz’, 1981.
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1. Comparative Analysis of Authentication Using Formant Features of Vowels and Consonants;Springer Proceedings in Mathematics & Statistics;2024
2. Using Formant Characteristics of Russian Vowels and Consonants for User Authentication;The Herald of the Siberian State University of Telecommunications and Information Science;2023-12-17
3. Using Cluster of Algorithms for Biometric Authentication;2023 International Russian Automation Conference (RusAutoCon);2023-09-10
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