A Speech Recognition Method Using Long Short-Term Memory Network in Low Resources

A speech recognition method using long short-term memory network in low resources (LSTM-LRASR method) is proposed to solve the problem that the recognition rate of an auto speech recognition system is declining due to the lack of transcripted training data in low resource environments. The method uses long short-term memory network to construct an acoustic model, and improves the low resource speech recognition performance from three aspects. These are feature extraction, data augmentation and model optimization. The feature extraction extracts language-independent high-level robustness parameters to reduce the dependence of acoustic model on training data. The data augmentation processes the transcripted data by speed perturbation, while the untranscripted data is recognized automatically, so that more transcripted data are created. The model optimization uses the sequential discriminating training technique to improve the ability of distinguishing phonemes, and the minimum Bayes-risk decoding is used to combine multiple systems and to further improve the recognition performance. The experimental results on the OpenKWS16 evaluation database show that the word error rate of the low resource speech recognition system built by the proposed LSTM-LRASR method is 29.9% lower than that of the baseline system, and the actual value weighted value increases by 60.3%. © 2017, Editorial Office of Journal of Xi'an Jiaotong University. All right reserved.