A new linear predictive method for spectral estimation of voiced speech

Author

Alku, Paavo ; Varho, Susanna

Author_Institution

Dept. of Appl. Phys., Turku Univ., Finland

Volume

4

fYear

1997

fDate

9-12 Jun 1997

Firstpage

2649

Abstract

A new linear predictive method for analysis of voiced speech is presented. The new technique, Separated Linear Prediction (SLP), is based on predicting sample x(n) from its previous samples as in conventional Linear Prediction (LP). SLP, when compared to conventional LP-analysis, separates p+1 previous samples into two groups: (a) x(n-1) and (b) x(n-1-i), 1⩽i⩽p. Sample x(n-1) is treated differently since it most likely has the highest correlation with sample x(n). By using linear extrapolation between x(n-1) and each of the samples in group (b) a new prediction model is formulated. By minimizing the square of the prediction error an optimal SLP-predictor is derived. When analyzing voiced speech it shown that SLP yields more accurate higher formants in comparison to conventional LP

Keywords

correlation methods; extrapolation; least squares approximations; prediction theory; spectral analysis; speech processing; Finnish language; all-pole modeling technique; autocorrelation criterion; higher formants; inverse filter; linear extrapolation; linear predictive method; optimal SLP-predictor; prediction model; separated linear prediction; spectral estimation; square minimization; voiced speech; Entropy; Extrapolation; Land mobile radio; Physics; Predictive models; Speech analysis; Speech coding; Speech processing; Speech recognition; Yield estimation;

fLanguage

English

Publisher

ieee

Conference_Titel

Circuits and Systems, 1997. ISCAS '97., Proceedings of 1997 IEEE International Symposium on

Print_ISBN

0-7803-3583-X

Type

conf

DOI

10.1109/ISCAS.1997.612869

Filename

612869