IMPROVING ENVIRONMENTAL ROBUSTNESS IN LARGE VOCABULARY SPEECH RECOGNITION
P.C. Woodland, M.J.F. Gales and D. Pye
This paper describes techniques to improve the robustness of the HTK large vocabulary speech recognition system to non-ideal acoustic environments. The primary methods are single-pass retraining using stereo training data; parallel model combination which combines HMMs trained on clean data with estimates of convolutional and additive noise; and maximum likelihood linear regression which estimates a set of linear transformations of the model parameters to the current conditions. Experiments are reported on both the 1994 ARPA CSR S5 (alternate microphones) and S10 (additive noise) spoke tasks and the 1995 ARPA CSR H3 task (multiple unknown microphones). The HTK system yielded the lowest error rates in both the H3-P0 and H3-C0 tests.
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