Improving the Method for Measuring the Accuracy Indicator of a Speech Signal Autoregression Model

The problem of determining the accuracy of an autoregressive model of a speech signal is considered, and a method for measuring the accuracy index in the sliding observation window mode is proposed. As an indicator of accuracy, we used a modified value of the COSH-distance (hyperbolic cosine) of the autoregressive model relative to the eponymous (single phoneme) Schuster periodogram as a reference spectral sample. To study the possibilities of the proposed method, a full-scale experiment was set up and carried out, in which the object of study was a set of autoregressive models of different orders. These models were obtained by Berg’s method for the vowel speech sounds of a test speaker. According to the results of the performed measurements for each vowel, the optimal values of the autoregressive order and the corresponding optimal autoregressive model were found. It is shown that this optimization made it possible to increase the accuracy of the autoregressive model of the speech signal by more than 60%, depending on the sound of the test speaker's speech and the characteristics of his vocal tract. The results obtained are intended for use in automatic processing and digital speech transmission systems with radical data compression based on linear prediction coefficients.