Improved robustness for speech recognition under noisy conditions using correlated parallel model combination

The parallel model combination (PMC) technique has been shown to achieve very good performance for speech recognition under noisy conditions. In this approach, the speech signal and the noise are assumed uncorrelated during modeling. In this paper, a new correlated PMC is proposed by properly estimating and modeling the nonzero correlation between the speech signal and the noise. Preliminary experimental results show that this correlated PMC can provide significant improvements over the original PMC in terms of both the model differences and the recognition accuracies. Error rate reduction on the order of 14% can be achieved.