Evaluation of universal compensation on Aurora 2 and 3 and beyond

A new method, namely universal compensation (UC), is introduced for speech recognition involving additive noise assuming no knowledge about the noise. The UC method involves a novel combination of the principle of multi-condition training and the principle of the missing-feature method. Multicondition training is employed to convert full-band spectral corruption into partial-band spectral corruption through compensations for simulated wide-band noise, and the missing-feature principle is employed to reduce the effect of the remaining partial-band corruption on recognition by basing the recognition mainly on the matched or appropriately compensated spectral components. This combination makes the new method potentially capable of dealing with any additive noise – with arbitrary temporal-spectral characteristics – based only on clean speech training data and simulated noise data, without requiring knowledge about the noise. This paper describes the evaluation of the UC method on Aurora 2 and 3 and further, on noise conditions unseen in the Aurora tasks. The results show that the new model assuming no knowledge of noise has performed equally well as the baseline models trained for the specific tasks. The new model has outperformed the baseline when there exists a mismatch between the training and testing conditions.