Replacing uncertainty decoding with subband re-estimation for large vocabulary speech recognition in noise

In this paper, we propose a novel approach for parameterized model compensation for large-vocabulary speech recognition in noisy environments. The new compensation algorithm, termed CMLLR-SUBREST, combines the model-based uncertainty decoding (UD) with subspace distribution clustering hidden Markov modeling (SDCHMM), so that the UD-type compensation can be realized by re-estimating the models based on small amount of adaptation data. This avoids the estimation of the covariance biases, which is required in model-based UD and usually needs a numerical approach. The Aurora 4 corpus is used in the experiments. We have achieved 16.9% relativeWER (word error rate) reduction over our previous missing-feature (MF) based decoding and 16.1% over the combination of Constrained MLLR compensation and MF decoding. The number of model parameters is reduced by two orders of magnitude.