A method for coding turbulent sound sources based on a hybrid linear prediction model

Within the framework of a current area of research in the fi eld of speech acoustics – non-invasive analysis of speech production processes – the acute problem of insuffi cient accuracy of parametric methods for coding a turbulent (noise) type voice source is considered. In order to overcome this problem, a method for coding a sound source with increased accuracy has been developed, based on a hybrid model of linear speech prediction, which combines the advantages of parametric and nonparametric approaches to speech signal modeling. In this case, the parametric approach is implemented in the form of a vector of linear prediction coeffi cients, and the nonparametric approach is implemented in the form of a clipped sequence of linear prediction error samples. Using the author's software, a full-scale experiment on a set of the whispered speech sounds of the control speaker has been set up and carried out. Compared to a known method for encoding a turbulent sound source based on a noise-excited linear prediction model, the developed method is characterized by an accuracy gain of 2.5 dB or more in the mean square error of linear prediction metric, while guaranteeing speaker voice recognition from the decoded (reconstructed) speech signal. The obtained results will be useful in developing low-cost systems and technologies for digital processing, synthesis, and transmission of speech with multiple data compression. Promising applications of the developed method include digital voice biometrics systems, in which speaker voice recognition is a key requirement for the speech signal encoding method.

1. Rabiner L. R., Shafer R. W. Theory and applications of digital speech processing, Pearson, London (2011).

2. Kadiri S. R., Alku P., Yegnanarayana B. Extraction and utilization of excitation information of speech: A Review. Proceedings of the IEEE, 109(12), 1920–1941 (2021). https://doi.org/10.1109/JPROC.2021.3126493 ; https://elibrary.ru/zszohh

3. Narendra N. P., Alku P. Glottal source information for pathological voice detection. IEEE Access, 8, 67745–67755 (2020). https://doi.org/10.1109/ACCESS.2020.2986171 ; https://elibrary.ru/msllfs

4. Ternström S. Special issue on current trends and future directions in voice acoustics measurement. Applied Sciences, 13(6), 3514 (2023). https://doi.org/10.3390/app13063514 ; https://elibrary.ru/arbxou

5. Savchenko V. V., Savchenko L. V. Method for measuring voice source parameters for linear predictive speech coding systems. Izmeritel’naya Tekhnika, 74(6), 74–84 (2025). (In Russ.) https://doi.org/10.32446/0368-1025it.2025-6-74-84 ; https://elibrary.ru/swdckd

6. Chen J. H., Thyssen J. Analysis-by-synthesis speech coding. In: Benesty J., Sondhi M. M., Huang Y. A. (eds) Springer Handbook of Speech Processing. Springer, Berlin, Heidelberg, 351–392 (2008). https://doi.org/10.1007/978-3-540-49127-9_17

7. Cabral J. P., Richmond K., Yamagishi J., Renals S. Glottal spectral separation for speech synthesis. IEEE Journal of Selected Topics in Signal Processing, 8(2), 195–208 (2014). https://doi.org/10.1109/JSTSP.2014.2307274

8. Savchenko V. V. Method for reduction of speech si gnal autoregression model for speech transmission systems on low-speed communication channels. Radioelectronics and Communications Systems, 64(11), 592–603 (2021). https://doi.org/10.3103/S0735272721110030 ; https://elibrary.ru/ruvjvs

9. Savchenko V. V., Savchenko L. V. Method of voice source coding with data compression based on the linear prediction model. Izmeritel’naya Tekhnika, 74(3), 67–78 (2025). (In Russ.) https://doi.org/10.32446/0368-1025it.2025-3-67-78 ; https://elibrary.ru/deyysw

10. Li Y., Tao J., Erickson D., Liu B., Akagi M. F0-noise-robust glottal source and vocal tract analysis based on ARX-LF model. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 29, 3375–3383 (2021). https://doi.org/10.1109/TASLP.2021.3120585 ; https://elibrary.ru/qjzvfy

11. Perrotin O., McLoughlin I. A spectral glottal flow model for source-filter separation of speech. 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, UK, 7160–7164 (2019). https://doi.org/10.1109/ICASSP.2019.8682625

12. Tokuda I. The source-filter theory of speech. Oxford Research Encyclopedia of Linguistics, Oxford (2021). https://doi.org/10.1093/acrefore/9780199384655.013.894

13. Gibson J. D. Mutual information, the linear prediction model, and CELP voice codecs. Information, 10(5), 179 (2019). https://doi.org/10.3390/info10050179

14. Zalazar I. A., Alzamendi G. A., Schlotthauer G. Symmetric and asymmetric Gaussian weighted linear prediction for voice inverse filtering. Speech Communication, 159, 103057 (2024). https://doi.org/10.1016/j.specom.2024.103057 ; https://elibrary.ru/hhmcjs

15. Savchenko V. V., Savchenko L. V. A method for the asynchronous analysis of a voice source based on a two-level autoregressive model of speech signal. Izmeritel’naya Tekhnika, 73(2), 55–62 (2024). (In Russ.) https://doi.org/10.32446/0368-1025it.2024-2-55-62 ; https://elibrary.ru/ivulbm

16. Savchenko V. V., Savchenko L. V. Method of a voice source acoustic analysis in real time. Izmeritel’naya Tekhnika, 74(4), 64–73 (2025). (In Russ.) https://doi.org/10.32446/0368-1025it.2025-4-64-73 ; https://elibrary.ru/grqhlg

17. Galunov V. I., Tampel’ I. B. The mechanism of operation of the voice source. Akusticheskiy zhurnal, 27(3), 321–334 (1981). (In Russ.)

18. Ramirez M. A. Modeling the unvoiced component in the canonical representation of speech. 16th International Conference on Digital Signal Processing, Santorini, Greece (2009). https://doi.org/10.1109/ICDSP.2009.5201232

19. Ahmadi F., McLoughlin I. V., Sharifzadeh H. R. Analysis-by-synthesis method for whisper-speech reconstruction. In: APCCAS 2008 – IEEE Asia Pacific Conference on Circuits and Systems. IEEE, 1280–1283 (2008). https://doi.org/10.1109/APCCAS.2008.4746261

20. Al-Heeti M. M., Hammad J. A., Mustafa A. S. Voice encoding for wireless communication based on LPC, RPE, and CELP. International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA), Ankara, Turkey (2022). https://doi.org/10.1109/HORA55278.2022.9800026

21. Gudnason J., Thomas M. R. P., Ellis D. P.W., Naylor P. A. Data-driven voice source waveform analysis and synthesis. Speech Communication, 54(2), 199–211 (2012). https://doi.org/10.1016/j.specom.2011.08.003

22. Drugman T., Alku P., Alwan A., Yegnanarayana B. Glottal source processing: From analysis to applications. Computer Speech & Language, 28(5), 1117–1138 (2014). https://doi.org/10.1016/j.csl.2014.03.003

23. Tan L., Jiang J. Waveform quantization and compression. In Digital Signal Processing (3rd Edition). L. Tan, J. Jiang (Eds), Academic Press, 475–527 (2019). https://doi.org/10.1016/B978-0-12-815071-9.00010-5

24. Chaouch H., Merazka M. Multiple description coding technique to improve the robustness of ACELP based coders AMR-WB. Speech Communication, 108, 33–40 (2019). https://doi.org/10.1016/j.specom.2019.02.002

25. Al-Radhi M. S., Abdo O., Csapó T. G., Abdou Sh., Németh G., Fashal M. A continuous vocoder for statistical parametric speech synthesis and its evaluation using an audio-visual phonetically annotated Arabic corpus. Computer Speech & Language, 60, 101025 (2020). https://doi.org/10.1016/j.csl.2019.101025 ; https://elibrary.ru/auphsu

26. Savchenko V. V., Savchenko L. V. Speech signal conversion method to improve speech intelligibility. Radiotekhnika i elektronika, 70(8), 753–760 (2025). (In Russ) https://doi.org/10.7868/S3034590125080062 ; https://elibrary.ru/ldijzq

27. Marple S. L. Digital spectral analysis with applications. 2nd ed. Dover Publications, Mineola, New York (2019).

28. Dzerjinsky R. I., Panov A. V., Sazonov A. I. Analysis and forecasting of microprocessor Performance Dynamics. In: Silhavy R., Silhavy P. (eds) Software engineering methods design and application. Proceedings of 13th Computer Science Online Conference 2024, Volume 1. Springer, Cham, 703–726 (2024). https://doi.org/10.1007/978-3-031-70285-3_55

29. Cao T., Xiang C., Wu Y., Zhang Y. Acoustic characteristics of whispered vowels: A dynamic feature exploration. Applied Acoustics, 228, 110362 (2025). https://doi.org/10.1016/j.apacoust.2024.110362 ; https://elibrary.ru/dsqoqi

30. Jesus L. M. T., Castilho S., Ferreira A., Costa M. C. Discriminative segmental cues to vowel height and consonantal place and voicing in whispered speech. Journal of Phonetics, 97, 101223 (2023). https://doi.org/10.1016/j.wocn.2023.101223 ; https://elibrary.ru/aytppp

31. Sayood K. Introduction to data compression. 5th ed., Morgan Kaufmann, Waltham (2017).

32. Savchenko V. V., Savchenko A. V. Method for measuring distortions in speech signals during transmission over a communication channel to a biometric identification system. Izmeritel’naya Tekhnika, (11), 65–72 (2020). (In Russ.) https://doi.org/10.32446/0368-1025it.2020-11-65-72 ; https://elibrary.ru/izwdoc

33. Lezin Yu. S. Introduction to the theory and technique of radio engineering systems. Radio i svyaz’, Moscow (1986). (In Russ.)

34. Molla M. K. I., Hirose K., Hasan M. K. Voiced/non-voiced speech classification using adaptive thresholding with bivariate EMD. Pattern Analysis and Applications, 19, 139–144 (2016). https://doi.org/10.1007/s10044-015-0449-3