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