In the paper, we propose a robust training strategy to deal with extraneous

acoustic variations for conversational speech recognition.

This strategy generalizes speaker adaptive training, where HMM

parameter transformations are used to normalize the extraneous

variations in the training data according to a set of pre-defined conditions.

Then a compact model and the associated prior p.d.f.’s of

transformation parameters are estimated using the maximum likelihood

criterion. In the testing phase, the compact model and the

prior p.d.f.’s are used to search for the unknown word sequence

based on Bayesian Prediction Classification. The proposed strategy

is evaluated in a Switchboard task to deal with pronunciation

variations in spontaneous speech recognition. Preliminary results

show moderate word error rate reduction over a well-trained baseline

system under identical experimental conditions.

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In  Proc. of the Int. Conf. on Spoken Language Processing

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