Linear prediction coefficients correction method for digital speech processing systems with data compression based on the autoregressive model of a voice signal

Мұқаба

Дәйексөз келтіру

Толық мәтін

Ашық рұқсат Ашық рұқсат
Рұқсат жабық Рұқсат берілді
Рұқсат жабық Рұқсат ақылы немесе тек жазылушылар үшін

Аннотация

The problem of distortion of the autoregressive model of the voice signal under the influence of additive background noise in digital speech processing systems with data compression based on linear prediction is considered. In the frequency domain, these distortions are observed in the weakening of the main formants responsible for the intelligibility of the speaker’s speech. To compensate for formant attenuation, it is proposed to modify the parameters of the autoregressive model (linear prediction coefficients) using the impulse response of a recursive shaping filter. Along with the amplitude amplification of the formants, their frequencies remain unchanged to make the speaker’s voice recognizable. The effectiveness of the method was studied experimentally using specially developed software. Based on the experimental results, conclusions were drawn about a significant increase in the relative level of formants in the power spectrum of the corrected voice signal.

Толық мәтін

Рұқсат жабық

Авторлар туралы

V. Savchenko

Editorial office of the journal “Radio Engineering and Electronics”

Хат алмасуға жауапты Автор.
Email: vvsavchenko@yandex.ru
Ресей, Mokhovaya St., 11, bldg. 7, Moscow, 125009

L. Savchenko

National Research University Higher School of Economics

Email: vvsavchenko@yandex.ru
Ресей, B. Pecherskaya St., 25, Nizhny Novgorod, 603155

Әдебиет тізімі

  1. Rabiner L.R., Schafer R.W. // Foundations and Trends in Signal Processing. 2007. V. 1. № 1–2. P. 1. https://doi.org/10.1561/2000000001
  2. O’Shaughnessy D. // J. Audio. Speech. Music Processing. 2023. V. 8. https://doi.org/10.1186/s13636-023-00274-x
  3. Savchenko V.V. // Radioelectron. Commun. Systems. 2021. V. 64. № 11. P. 592. https://doi.org/10.3103/S0735272721110030
  4. Gibson J. // Information. 2019. V. 10. № 5. 179. https://doi.org/10.3390/info10050179
  5. Chaouch H., Merazka F., Marthon Ph. // Speech Commun. 2019. V. 108. P. 33. https://doi.org/10.1016/j.specom.2019.02.002.
  6. Савченко В.В., Савченко Л.В. // Измерит. техника. 2019. № 9. С. 59. https://doi.org/10.32446/0368-1025it.2019-9-59-64
  7. Candan Ç. // Signal Processing. 2020. V. 166. № 10. Р. 107256. https://doi.org/10.1016/j.sigpro.2019.107256
  8. Semenov V.Yu. // J. Automation and Inform. Sci. 2019. V. 51. № 2. P. 30. https://doi.org/10.1615/JAutomatInfScien.v51.i2.40
  9. Marple S.L. Digital Spectral Analysis with Applications. 2-nd ed. Mineola: Dover Publ., 2019.
  10. Burg J.P. Maximum entropy spectral analysis. PhD Thesis. Stanford Univ., 1975.
  11. Magi C., Pohjalainen J., Bäckström T., Alku P. // Speech Commun. 2009. V. 51. № 5. P. 401. https://doi.org/10.1016/j.specom.2008.12.005
  12. Rout J.K., Pradhan G. // Speech Commun. 2022. V. 144. P. 101. https://doi.org/10.1016/j.specom.2022.09.004
  13. Deng F., Bao Ch. // Speech Commun. 2016. V. 79. P. 30. https://doi.org/10.1016/j.specom.2016.02.006
  14. Савченко В.В., Савченко А. В. // Измерит. техника. 2020. № 11. С. 65. https://doi.org/10.32446/0368-1025it.2020-11-65-72
  15. Савченко В.В. // РЭ. 2023. Т. 68. № 2. С. 138. https://doi.org/10.31857/S0033849423020122
  16. Kathiresan Th., Maurer D., Suter H., Dellwo V. // J. Acoust. Soc. Amer. 2018. V. 143. № 3. P. 1919. https://doi.org/10.1121/1.5036258
  17. Ngo Th., Kubo R., Akagi M. // Speech Commun. 2021. V. 135. P. 11. https://doi.org/10.1016/j.specom.2021.09.004
  18. Palaparthi A., Titze I. R. // Speech Commun. 2020. V. 123. P. 98. https://doi.org/10.1016/j.specom.2020.07.003
  19. Sadasivan J., Seelamantula Ch.S., Muraka N.R. // Speech Commun. 2020. V. 116. P. 12. https://doi.org/10.1016/j.specom.2019.11.001
  20. Gustafsson Ph.U., Laukka P., Lindholm T. // Speech Commun. 2023. V. 146. P. 82. https://doi.org/10.1016/j.specom.2022.12.001
  21. Ito M., Ohara K., Ito A., Yano M. // Proc. Interspeech. 2010. V. 2490. https://doi.org/10.21437/Interspeech.2010-669
  22. Arun-Sankar M.S., Sathidevi P. S. // Heliyon. 2019. V. 5. № 5. Р. e01820. https://doi.org/10.1016/j.heliyon.2019.e01820
  23. Narendra N.P., Alku P. // Speech Commun. 2019. V. 110. P. 47. https://doi.org/10.1016/j.specom.2019.04.003
  24. Alku P., Kadiri S.R., Gowda D. // Computer Speech & Language. 2023. V. 81. № 10. Р. 101515. https://doi.org/10.1016/j.csl.2023.101515
  25. Sadok S., Leglaive S., Girin L. et al. // Speech Commun. 2023. V. 148. P. 53. https://doi.org/10.1016/j.specom.2023.02.005
  26. Nguyen D.D., Chacon A., Payten Ch.L. et al. // Int. J. Language & Commun. Disorders. 2022. V. 57. № 2. P. 366. https://doi.org/10.1111/1460-6984.12705

Қосымша файлдар

Қосымша файлдар
Әрекет
1. JATS XML
2. Fig. 1. Estimation of the envelope of the SPM (3) signal of the vowel phoneme “a” with the SNR q2 equal to 0 (1), 10 (2) and 20 dB (3).

Жүктеу (56KB)
3. Fig. 2. Estimates of the KLP of the phoneme “a” signal with the q2 SNR equal to 0 (1), 10 (2) and 20 dB (3) in comparison with the KLP vector in the absence of noise (dotted line).

Жүктеу (67KB)
4. Fig. 3. Pulse response (5) of the forming filter (4) at SNR q2 equal to 0 (1), 10 (2) and 20 dB (3).

Жүктеу (206KB)
5. Fig. 4. Corrected impulse response (6) at c = 0.01 (1), 0.03 (2) and 0.05 (3) for the case of equal SNR q2 = 0 dB in comparison with the impulse response (5) in the absence of correction (dotted line).

Жүктеу (190KB)
6. Fig. 5. The envelope of the SPM (3) of the synthesized voice signal at c = 0.01 (1), 0.03 (2) and 0.05 (3) for the case of equal SNR q2 = 0 dB and in the absence of correction (dotted line).

Жүктеу (43KB)
7. Fig. 6. Fragments of the synthesized signal of the vowel phoneme “a" in c = 0.01 (1), 0.03 (2) and 0.05 (3) for the case of equal SNR q2 = 0 dB and in the absence of correction (dotted line).

Жүктеу (134KB)
8. 7. Schuster periodogram (10) of the vowel phoneme “a” signal synthesized according to the AR model (2) at c = -0.06 (solid curve) and c = 0 (dotted line).

Жүктеу (167KB)
9. 8. Schuster periodogram (10) of the signal of the fricative sound of speech “w" synthesized according to the AR model (2) at c = 0.06 (solid curve) and c = 0 (dotted line).

Жүктеу (169KB)

© Russian Academy of Sciences, 2024