I. Introduction
Autoregressive (AR) modeling is widely used for power spectrum estimation [1], [2]. An order- AR model, also referred to as linear prediction model, is defined by , where is the data sequence and is a random sequence of variance . The power spectral density, or power spectrum, of the order- AR model is given by \eqalignno{P_{X}^{\rm AR} (f) & = {\mbi \sigma}_{e}^{2} \cdot \left\vert {1 + \sum_{k = 1}^{P} {a_{k} \exp (- j2\pi fk)}} \right\vert ^{- 2}\cr & & {\hbox {for}}\ 0 \leq f \leq 1. }From the aspect of filtering, an AR model describes a data sequence generated from an all-pole filter fed by a white excitation sequence. As a source-filter model with low dimension parameters, the AR modeling method has been successfully utilized to model the acoustical system of speech production, which comprises the vocal tract and the glottal excitation, and has become an essential method in many speech applications, such as speech coding, speech recognition, pitch tracking, and formant estimation.