Universal compensation -- an approach to noisy speech recognition assuming no knowledge of noise
Ji Ming · 2004
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.