Robust speech recognition using missing feature theory in the cepstral or LDA domain

When applying Missing Feature Theory to noise robus t speech recognition, spectral features are labeled a s either reliable or unreliable in the time-frequency plane. The acoustic model evaluation of the unreliable feature s is modified to express that their clean values are unk nown or confined within bounds. Classically, MFT requires a n assumption of statistical independence in the spect ral domain, which deteriorates the accuracy on clean speech. In t is paper, MFT is expressed in any domain that is a linear tra nsform of (log-)spectra, for example for cepstra and their ti mederivatives. The acoustic model evaluation is recas t as a nonnegative least squares problem. Approximate solutio ns are proposed and the success of the method is shown thr oug experiments on the AURORA-2 database.