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It is very important to exploit abundant unlabeled speech for improving the acoustic model training in automatic speech recognition (ASR). Semi-supervised training methods incorporate unlabeled data in addition to labeled data to enhance the model training, but it encounters the error-prone label problem. The ensemble training scheme trains a set of models and combines them to make the model more...
While the performance of ASR systems depends on the size of the training data, it is very costly to prepare accurate and faithful transcripts. In this paper, we investigate a semisupervised training scheme, which takes the advantage of huge quantities of unlabeled video lecture archive, particularly for the deep neural network (DNN) acoustic model. In the proposed method, we obtain ASR hypotheses...
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