Missing Feature Theory (MFT), a powerful systematic framework for robust speech recognition, to date has not been optimally applied to linear-transform based features like MFCC or HLDA, which are necessary for state-of-the-art recognition accuracy, due to the intractable multivariate integral in bounded marginalization. This paper seeks to enable more optimal use of MFT with MFCC features through two approximations of this integral: Numeric integration by linear sampling, and approximation by the integrand's maximum. The former is made feasible through a "tridiagonal" approximation of MFCC, based on interpreting MFCC as bandpass-filtering the filterbank vector. The latter is solved through quadratic programming. Their effectiveness is shown for recognizing reverberated TIMIT speech utilizing temporal auditory masking.
Bibliographic reference. Seide, Frank / Zhao, Pei (2010): "On using missing-feature theory with cepstral features - approximations to the multivariate integral", In INTERSPEECH-2010, 2094-2097.