Kalman-filtering speech enhancement method based on a voiced-unvoiced speech model

In this work, we are concerned with optimal estimation of clean speech from its noisy version based on a speech model we propose. We first propose a ( single) speech model which satisfactorily describes voiced and unvoiced spe ech and silence (i.e., pauses between speech utterances), and also allows f or exploitation of the long term characteristics of noise. We then reformul ate the model equations so as to facilitate subsequent application of the w ell-established Kalman filter for computing the optimal estimate of the cle an speech in the minimum-mean-square-error sense. Since the standard algori thm for Kalman filtering involves multiplications of very large matrices an d thus demands high computational cost, we devise a mathematically equivale nt algorithm which is computationally much more efficient, by exploiting th e sparsity of the matrices concerned. Next, we present the methods me use f or estimating the model parameters and give a complete description of the e nhancement process. Performance assessment based on spectrogram plots, obje ctive measures and informal subjective listening tests all indicate that ou r method gives consistently good results. As far as signal-to-noise ratio i s concerned, the improvements over existing methods can be as high as 4 dB.