|Department of Engineering|
|University of Cambridge > Engineering Department > Machine Intelligence Lab|
UNCERTAINTY DECODING FOR NOISE ROBUST SPEECH RECOGNITION
Hank Liao and Mark Gales
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^Lcially 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.
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2005 Cambridge University Engineering Dept
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