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Method for Reduction of Speech Signal Autoregression Model for Speech Transmission Systems on Low-Speed Communication Channels

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Abstract

In this paper it is considered the problem of reduction or reduction of the order p ≫ 1 of an autoregressive model (AR-model) of a speech signal by the criterion of minimum loss of useful information. The problem is formulated as an optimization problem in terms of discrete spectral modeling. It is indicated that the most acute problem in solving is the necessity to scale the AR-model parameters for the simulated signal at each step of iterative calculation process. To overcome this problem, it is proposed to use the measure of information divergence of signals in the frequency domain with the property of scale invariance as the goal functional. On its basis, a new method of the AR-model reduction is developed where the scaling operation exceeds the limits of the iterative optimization procedure. The effectiveness of the proposed method is substantiated theoretically and researched experimentally. It is shown that the main component of the achieved effect is the gain in accuracy of the reduced AR-model in the Kullback–Leibler information metric. The results obtained are addressed to researchers and developers of systems and technologies for digital speech transmission over low-speed communication channels.

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Notes

  1. https//www.itu.int/rec/T-REC-G/en.

  2. https//dic.academic.ru/dic.nsf/ruwiki/614146.

  3. GOST R 50840-95 "Speech transmission over communication channels. Methods for assessing quality, legibility and recognition".

  4. GOST R 51061-97 Systems of low-speed speech transmission over digital channels. Speech quality options and measurement methods.

  5. Taking into account the features of the acoustic mechanism of speech production [1] and AR-model (1), in this case, the possibility of equality to zero of both the PSD G(F) and its estimate \( \hat{G}(f) \) are excluded from consideration throughout their definition area.

  6. An increase in the convergence rate of iterations (9) by increasing the step γ0 is accompanied [24] by the inevitable decrease in the accuracy of the vector of final approximations bp+1(L).

  7. https://sites.google.com/site/frompldcreators/produkty-1/phonemetraining.

  8. http://www.itu.int/rec/T-REC-G.728-201206-I/en.

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Authors and Affiliations

  1. Linguistic University of Nizhny Novgorod, Nizhny Novgorod, Russian Federation

    V. V. Savchenko

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ADDITIONAL INFORMATION

V.V. Savchenko

The author declares that he has no conflicts of interest.

This article does not contain any studies with human participants or animals performed by any of the authors.

The initial version of this paper in Russian is published in the journal “Izvestiya Vysshikh Uchebnykh Zavedenii. Radioelektronika,” ISSN 2307-6011 (Online), ISSN 0021-3470 (Print) on the link http://radio.kpi.ua/article/view/S0021347021110030 with DOI: https://doi.org/10.20535/S0021347021110030

Additional information

Translated from Izvestiya Vysshikh Uchebnykh Zavedenii. Radioelektronika, No. 11, pp. 682-695, November, 2021 https://doi.org/10.20535/S0021347021110030 .

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Savchenko, V.V. Method for Reduction of Speech Signal Autoregression Model for Speech Transmission Systems on Low-Speed Communication Channels. Radioelectron.Commun.Syst. 64, 592–603 (2021). https://doi.org/10.3103/S0735272721110030

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