非Toeplitz型自己相関行列を用いた音声の線形予測分析

概要

論文の詳細を見る
A covariance method is proposed to analyze speech more accurately than an autocorrelation method. The new covariance method, called the non-Toeplitz form method, ensures uniqueness and stability of data analysis. First, forward and backward prediction models are presented. The uniqueness of definitions of a data vector and a prediction error vector is proved. Second, prediction procedures based on Gram-Schmidt orthogonalization are discussed, and new sample autocorrelation functions are defined. Third, prediction procedures for solving normal equations by using matrix decomposition are presented. An equivalence relationship between auxiliary variables in this procedure and PARCOR coefficients is also discussed. Fourth, the positive definiteness of an autocorrelation matrix whose elements are given by the autocorrelation function proposed in this paper is shown. Finally, the proposed method is compared with other methods.
社団法人日本音響学会の論文