Abstract：For speech recognition of low resources, a selection strategy for semi-supervised learning with a large number of unlabeled data is proposed, and this strategy is applied to both acoustic modeling and language modeling. After a small amount of data is used to train the seed model, the unlabeled data is decoded using the seed model. Firstly, high-confidence sentences are selected by using a combination of confidence measure and perplexity in the decoded best candidate results. Then, the high-confidence sentences are used to train acoustic model and language model. Furthermore, the decoded lattice is transformed to obtain multiple candidate texts for language model training. In the Japanese recognition task, the proposed method obtains a better recognition rate than the method of selecting data based on confidence measure.
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