Spectral smoothing technique in PARCOR speech analysis-synthesis

In linear predictive analysis of speech, voice periodicity influences formant frequency and bandwidth estimation accuracy. One of the most serious errors in estimating formant parameters is bandwidth underestimation that causes a quality difference between synthetic and natural speech. In this paper, the spectral smoothing technique (SST), using a lag window, is introduced in an autocorrelation method of the linear predictive analysis. In order to assess the effectiveness of the SST to reduce estimation errors, experimental comparisons of the usual autocorrelation method and the SST are presented. Spectral sensitivity analysis is also presented to evaluate the SST from the viewpoint of parameter quantization properties. SST features are summarized as follows: 1) Bandwidth underestimation elimination. 2) Spectral sensitivity reduction of PARCOR coefficients. 3) Simplicity in hardware implementation.