Combining the union model and missing feature method to improve noise robustness in ASR

In this paper, we propose a combination of the probabilistic union model and the missing feature method to improve noise robustness in ASR. Specifically, the missing feature method is employed to eliminate the acoustic frames in which all frequency bands are corrupted and the union model is employed to extract useful information from the frames involving only partial frequency -band corruption. This combination enhances the capability of the union model for dealing with wide-band noise corruption, and moreover, it reduces the dependence of the missing-feature method on information about the noise - only a knowledge on the bandwidth of the noise is required. We have tested the new system based on the TIDigits database, corrupted by various types of wide-band and narrow-band noise. The experimental results show significant performance improvement by the proposed system, compared to a previous model without using the combination.