Data selection from multiple ASR systems' hypotheses for unsupervised acoustic model training

This paper addresses unsupervised training of DNN acoustic model, by exploiting a large amount of unlabeled data with CRF-based classifiers. In the proposed scheme, we obtain ASR hypotheses by complementary GMM and DNN based ASR systems. Then, a set of dedicated classifiers are designed and trained to select the better hypothesis and verify the selected data. It is demonstrated that the classifiers can effectively filter usable data from unlabeled data for acoustic model training. The proposed method achieved significant improvement in the ASR accuracy from the baseline system, and it outperformed the models trained from the data selected based on the confidence measure scores (CMS) and also from the simple ROVER-based system combination.