Accurate Power Spectrum Estimation of Speech with Spectrum Compensation Based on Prediction Error Filtering
Arifour Rahman, Yosuke Sugiura, Tetsuya Shimamura · WSEAS Transactions on Signal Processing archive · 2017
This paper proposes a linear prediction (LP) method to estimate accurately the original power spectrum of the input speech signal. A prediction error filter (PEF) is used as a pre-processor, and the LP based power spectrum estimation is compensated by the frequency characteristics of the designed PEF. Through experiments on synthetic vowels, we show that the proposed spectrum compensation method can estimate the power spectrum more accurately than the direct and pre-emphasis LP methods.