Uncertainty Decoding for Noise Robust Automatic Speech Recognition
H. Liao and M.J.F. Gales
CUED/F-INFENG/TR-499. October 2004.

This report presents uncertainty decoding as a method for robust
automatic speech recognition for the Noise Robust Automatic Speech
Recognition project funded by Toshiba Research Europe Limited. The
effects of noise on speech recognition are reviewed and a general
framework for noise robust speech recognition introduced.  Common and
related noise robustness techniques are described in the context of
this framework. Uncertainty decoding is also presented in this
framework with the goal of providing fast noise compensation through
the propagation of uncertainty to the decoder.  Two forms are
discussed, the Joint and SPLICE methods, and evaluated on the medium
vocabulary Resource Management corpus at a range of articially
produced noise levels. It was found that the uncertainty decoding
algorithms did not meet the performance of a matched system, but were
more accurate than the baseline SPLICE enhancement technique and low
numbers of CMLLR transforms.