Predictive linear transforms for noise robust speech recognition

Abstract

It is well known that the addition of background noise alters the correlations between the elements of, for example, the MFCC feature vector. However, standard model-based compensation techniques do not modify the feature-space in which the diagonal covariance matrix Gaussian mixture models are estimated. One solution to this problem, which yields good performance, is joint uncertainty decoding (JUD) with full transforms. Unfortunately, this results in a high computational cost during decoding. This paper contrasts two approaches to approximating full JUD while lowering the computational cost. Both use predictive linear transforms to modify the feature-space: adaptation-based linear transforms, where the model parameters are restricted to be the same as the original clean system; and precision matrix modelling approaches, in particular semi-tied covariance matrices. These predictive transforms are estimated using statistics derived from the full JUD transforms rather than noisy data. The schemes are evaluated on AURORA 2 and a noise-corrupted resource management task.