Spectrum Compensation Method for Speech Signals Based on Prediction Error Filtering
Md. Arifour Rahman, Yosuke Sugiura, Tetsuya Shimamura · 2017
This paper proposes a technique for improving the performance of linear prediction (LP) by utilizing the prediction error filter (PEF) as a pre-processor. Problems often occur in estimating the power spectrum of the input speech signal using LP due to the large spectral dynamic range of speech which makes the autocorrelation matrix ill-conditioned. In the proposed method, the LP based power spectrum estimation is compensated by the spectrum characteristics of the designed PEF. The accuracy of formant frequency estimation is verified on synthetic speech. The validity of the proposed method is also illustrated by inspecting real air conducted and bone conducted speeches. Through the experiments, we show that the proposed method can estimate the power spectrum more accurately than the conventional direct and pre-emphasis LP methods