AN IMMUNOLOGICAL MODEL OF TEXT-INDEPENDENT VOICE IDENTIFICATION
Abstract
An immunological model of clonal selection with positive selection based on the principles
of mass-parallel data processing used in artificial immune systems, is proposed. The model is
designed for text-independent identification of a person by voice. In contrast to known passwordbased
voice identification systems, the proposed model implements decentralized recognition of
voice data by matching it with detectors that simulate immunocompetent cells of the immune system.
The initial voice features are generated in a linear speech predictor and are represented by
cepstral coefficients. The sequence of cepstral coefficients is further divided into equal time sections
- morphemes, which are abstract linguistic units that unify phonemes. Morphemes carry the
individual coloring of consecutive temporal segments of speech reproduced by the voice, allowingthem to be used productively as voice identifiers. The matching of voice morphemes with detectors
is carried out according to the principle of positive selection based on the Euclidean proximity
measure. The model's "friend-or-foe" identification decision making is implemented on the basis of
a statistical approach in terms of the frequency of detector response. The proposed model implements
the identification of the speaker's personality at the rate of receipt of his voice data. At the
same time, personality identification is invariant to the language, volume and content of speech.
The advantage of the model is complete protection against replay attacks. The effective realization
of the model, the accuracy and speed of identification are due to the possibility of organizing highspeed
analysis of large volumes of voice data, which in the long term corresponds to the pace of
development and application of high-performance computing systems.
References
IEEE Trans. Audio Speech Language Processing, 2008, Vol. 16, Issue 5, pp. 980-988.
2. Zhang Sh.-X, Mak M.-W. A new adaptation approach to high-level speaker-model creation in
speaker verification, Speech Communication, 2009, Vol. 51, pp. 534-550.
3. Pervushin E.A. Obzor osnovnykh metodov raspoznavaniya diktorov [Overview of the main methods
of speaker recognition], Matematicheskie struktury i modelirovanie [Mathematical structures
and modeling], 2011, Issue 24, pp. 41-54.
4. Grishin V.M., Kalashnikov D.M. Rechevoy fragmentator dlya neyrosetevogo biometricheskogo
vokodera [Speech fragmentator for neural network biometric vocoder], Penza-2012: Tr.
nauchno-tekhnicheskoy konferentsii klastera penzenskikh predpriyatiy, obespechivayushchikh
bezopasnost' informatsionnykh tekhnologiy [Penza-2012: Proceedings of the scientific and technical
conference of the cluster of Penza enterprises providing information technology security].
Available at: http://pniei.rf/activity/science/BIT/T8-p73.pdf.
5. Lei Y., Hansen J.H.L. Mismatch modeling and compensation for robust speaker verification
Speech Communication, 2011, Vol. 53, pp. 257-268.
6. Makarevich O.B., Babenko L.K., Fedorov V.M., Yurkov P.Yu. Tekstonezavisimaya
autentifikatsiya/identifikatsiya po golosu v sistemakh upravleniya dostupom [Text-independent authentication/
voice identification in access control systems], X Vserossiyskaya nauchnoprakticheskaya
konferentsiya «Problemy informatsionnoy bezopasnosti v sisteme vysshey shkoly»
[X All–Russian scientific and practical conference "Problems of information security in the
higher school system"]. Moscow: MIFI, 2003, pp. 28-29.
7. Mozharov G.P., Chebotarev R.S. Tekstonezavisimyy metod identifikatsii cheloveka po ego golosu
[Text-independent method of identifying a person by his voice], Inzhenernyy zhurnal: nauka i
innovatsii [Engineering Journal: Science and Innovation], 2012, No. 11, pp. 168-178.
8. Klimenko N.S. Razrabotka struktury tekstonezavisimoy sistemy identifikatsii diktora [Development
of the structure of a text–independent speaker identification system], Iskusstvennyy intellect [Artificial
Intelligence], 2012, No. 4, pp. 161-171.
9. Bimbot F. et al. A Tutorial on Text-Independent Speaker Verification, EURASIP Journal on
Applied Signal Processing, 2004, No. 4, pp. 430-451.
10. Dasgupta D. Artificial Immune Systems and Their Applications, Ed., Springer-Verlag, 1999.
11. De Castro L.N., Timmis, J.I. Artificial Immune Systems: A New Computational Intelligence
Approach, London: Springer-Verlag, 2000, 357 p.
12. Iskusstvennye immunnye sistemy i ikh primenenie [Artificial immune systems and their application],
ed. by D. Dasgupty: transl. from engl. by A.A. Romanyukhi. Moscow: Fizmatlit, 2006, 344
p.13. Bryukhomitskiy Yu.A., Federov V.M. Metod tekstonezavisimoy identifikatsii lichnosti po golosu
[The method of text-independent identification of a person by voice], Izvestiya YuFU.
Tekhnicheskie nauki [Izvestiya SFedU. Engineering Sciences], 2018, No. 8 (202), pp. 173-181.
14. Bryukhomitskiy Yu.A., Fedorov V.M. Immunologicheskiy podkhod k identifikatsii lichnosti po
golosu [Immunological approach to voice identification], Spetsial'naya svyaz' i bezopasnost'
informatsii» (SSBI – 2019): Mater. III mezhdunarodnoy nauchno-prakticheskoy konferentsii. Krasnodar,
18 fevralya 2019 g. [Special communication and information security" (SSBI – 2019):
Proceedings of the III International Scientific and Practical Conference. Krasnodar, February
18, 2019]. Krasnodar: Izd-vo: Krasnodarskiy TSNTI – filial FGBU «REA» Minenergo Rossii,
2019, pp. 24-30.
15. Bryukhomitskiy Yu.A. Tekstonezavisimaya identifikatsiya lichnosti po dinamicheskim
biometricheskim parametram na osnove immunnoy modeli klonal'noy selektsii [Text-independent
identification of a person by dynamic biometric parameters based on the immune model of
clonal selection], Izvestiya YuFU. Tekhnicheskie nauki [Izvestiya SFedU. Engineering Sciences],
2018, No. 5 (199), pp. 142-151.
16. Markel Dzh., Grey A.Kh. Lineynoe predskazanie rechi [Linear prediction of speech]: transl. from
engl. by, ed. by Yu.N. Prokhorova, V.S. Zvezdina. Moscow: Svyaz', 1980, 308 p.
17. Litvintseva A.V., Obolonin M.A. Ispol'zovanie lineynogo predskazatelya rechi v programmnoy
modeli nizkoskorostnogo vokodera dlya peredachi rechi po gidroakusticheskomu kanalu svyazi
[Using a linear speech predictor in a software model of a low-speed vocoder for speech transmission
over a hydroacoustic communication channel], Sovremennye problemy nauki i
obrazovaniya [Modern problems of science and education], 2013, No. 3. Available at:
https://science-education.ru/ru/article/view?id=9158 (accessed 04 April 2022).
18. Anden J., Mallat S. Multiscale Scattering for Audio Classification, Proceedings of the 12th
International Society for Music Information Retrieval Conference, ISMIR 2011, Miami, Florida,
USA, October 24-28, 2011.
19. Mallat S. Group Invariant Scattering, Communications in Pure and Applied Mathematics,
2012, Vol. 65, No. 10.
20. Bryukhomitskiy Yu.A. Immunologichekiy metod verifikatsii rukopisi s ispol'zovaniem vektornogo
predstavleniya dannykh [Immunological method of manuscript verification using vector representation
of data], Izvestiya YuFU. Tekhnicheskie nauki [Izvestiya SFedU. Engineering Sciences],
2016, No. 9 (182), pp. 50-57.
21. Bryukhomitskiy Yu.A. Immunologicheskiy podkhod k identifikatsii lichnosti po dinamicheskim
biometricheskim parametram [Immunological approach to identity identification by dynamic
biometric parameters] Izvestiya YuFU. Tekhnicheskie nauki [Izvestiya SFedU. Engineering Sciences],
2017, No. 5 (190), pp. 56-66.
