Hybrid Method of Speech Signals Spectral Analysis Based on the Autoregressive Model and Schuster Periodogram

Abstract

The task of measuring the spectral density of power of a speech signal in sliding observation window mode is examined. A parametric approach to solving this task using an autoregressive data model is studied. The problem of optimizing the order of an autoregressive model under the conditions of small samples is studied. It is proposed to solve the problem using a hybrid method of spectral analysis based on sequential enumeration of a finite number of variants. The optimization criterion is formulated in terms of an inverse problem: from the speech signal to the voice source. It uses the scale-invariant measure of the spectral distance as the objective function, and the Schuster periodogram as the reference sample. The effectiveness of the hybrid method has been experimentally evaluated on the basis of the author's software. It is shown that with the duration of the observation window no greater than 10 ms, the use of the hybrid method increases the accuracy of spectral analysis by more than 30%, compared to the well-known Berg method, the order of which is established according to the Akaike information criterion.