Universal compensation -- an approach to noisy speech recognition assuming no knowledge of noise

We aim to develop an acoustic model for noisy speech recognition that is "trained once, suits all", in terms of offering a recognition performance close to the matched training-testing condition performance based only on clean speech training data. This paper describes such a method termed universal compensation (UC), for its ability to accommodate arbitrary additive noise without assuming any knowledge about the noise. The new UC method consists of two parts: 1) converting full-band spectral corruption into partial-band spectral corruption through compensations for simulated wide-band flat-spectrum noise at consecutive SNRs (signal-to-noise ratios); and 2) reducing the effect of the remaining partial frequency-band corruption on recognition by ignoring the severely mismatched spectral components and basing the recognition mainly on the matched or appropriately compensated spectral components. Experiments on Aurora 2 indicate that the new model, trained from clean data, has achieved a performance comparable to the performance obtained by the baseline system trained on multi-condition data; experiments with noises unseen in Aurora 2 have shown significant improvement for the new model over the baseline model with multi-condition training.