Speech analysis by selective linear prediction in the time domain

Abstract

Linear prediction method is one of the most frequently used analysis methods of speech. Covariance method and auto-correlation method of linear prediction often fail to make a precise analysis of speech because of the excitation source or fundamental frequency. In order to decrease the affect of the excitation source, various kinds of difference operations are usually employed for preprocessing. However, such preprocessings do not always work satisfactorily. Here proposed is a new approach to LPC analysis based on selective use of speech data to reject the data disturbed by the excitation source, and is called selective linear prediction method. The method is constructed aiming to improve the accuracy of analysis. First, the formulation of linear prediction is presented using generalized inverse matrices. Then, a successive computation is described based on Givens' reduction. The selective computation, which plays an essential role in our method, owes its efficiency to Givens' reduction. Finally the advantage of the proposed method is demonstrated by computer simulation using both synthetic and natural speech.