Deconvolution and Vocal-tract Parameter Estimation of Speech Signals by Higher-order Statistics Based Inverse Filters

Abstract

In this paper, we propose a two-step method for deconvolution and vocal-tract parameter estimation of (nonGaussian) voiced speech signals. In the first step, the driving input (a non-Gaussian pseudo-periodic positive pulse train) to the vocal-tract filter which can be nonminimum-phase is estimated from speech data by a hagher-order statistics (HOS) based inverse fi lter. I n the second step, autoregressive moving average (ARMA) parameters of the vocal-tract filter are estimated with the estimated input and speech data by a prediction error system identification method (an input-output system adentification method). Finally, some experimental results with real speech data are provided to justify the good performance of the proposed method.

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Cite this paper

@inproceedings{Chen2004DeconvolutionAV, title={Deconvolution and Vocal-tract Parameter Estimation of Speech Signals by Higher-order Statistics Based Inverse Filters}, author={Wu-Ton Chen}, year={2004} }