Submitted to Robust’99, Tampere, Finland on March 5th 1999 ROBUST ASR WITH UNRELIABLE DATA AND MINIMAL ASSUMPTIONS

Human speech perception withstands a wide variety of distortions, both experimentally applied and naturallyoccurring. A novel approach to these situations in robust ASR identifies the spectro-temporal regions which carry reliable speech evidence and treats the remainder as missing or uncertain. This standpoint makes minimal assumptions about any noise background. This paper describes two approaches to the adaptation of continuous-density hidden Markov model-based speech recognisers to deal with missing and uncertain acoustic data. The first computes output probabilities on the basis of the reliable evidence only, while the second estimates values for the unreliable regions by conditioning on the reliable parts. Both techniques are evaluated on the TIDigits corpus for several NOISEX noise sources, using spectral subtraction to identify reliable regions. These studies demonstrate that the two schemes behave comparably, and that both produce a significant performance advantage over spectral subtraction alone.