This paper addresses the problem of integration of missing data theory in the context of robust speech recognition in additive noise. It shows that techniques based on statistical estimation and thresholding of a posteriori signal-to-noise ratio (SNR) can be used for the detection of reliable (not much affected by noise) features as opposed to unreliable or missing (masked by noise) features. In the paper, a statistical detector for reliable features is proposed and tested for several values of deterministic and probabilistic thresholds at very low SNRs (from 20 to-10 dB). The limitations of the detector are also studied and measures for the evaluation of the performance of such a detection are proposed. 1
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