Abstract：For acoustic modeling of small languages with rare resource, a perplexity based approach is proposed to select unsupervised data in the decoding transcription and retrain the acoustic model. The large unsupervised corpus is decoded using the initial acoustic model trained with a small amount of labeled data, and the perplexity between the decoded text and the training set is calculated. Then, the selected data similar to the labeled data are used to train the acoustic model along with the labeled data. To improve the correctness of the decoded unsupervised data,the final network parameters of acoustic model are adjusted by only using the correct labeled data in the last iteration during the training of model parameters based on deep neural network. In the VLLP recognition task of Swahili provided by NIST 2015 open keyword search competition, the proposed approach can improve the recognition rate compared with other methods.
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2016, Vol. 29 
