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Abstract
A new method for noise compensation for additive background noise based only on clean speech training data is described, assuming arbitrary noise characteristics. Experiments on Aurora 2 indicate that the new method has achieved a performance comparable to, or better than, the performance obtained by the baseline model trained on multi-condition data.
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