Speech Recognition Model in Civil Aviation's Radiotelephony Communication Based on BiLSTM Neural Networks

The radiotelephony communication is crucial for flight safety in civil aviation. The special grammatical structure and pronunciation in civil aviation radiotelephony communication makes the traditional acoustic model of speech recognition not suitable for civil aviation radiotelephony communication context. In order to model the acoustic pattern of radiotelephony communication of civil aviation, a speech recognition method based on Bidirectional Long Short-Term Memory (BiLSTM) neural networks is proposed in this paper. First, the FBANK acoustic feature that extracted from speech dataset of civil aviation radiotelephony communication is as input and the connectionist temporal classification (CTC) objective function is used for training multi-layer BiLSTM neural networks. Then, using the BiLSTM/CTC acoustic model, language model and lexicon to realize the auto speech recognition of civil aviation radiotelephony communication. Based on the combination of data augmentation and data migration, the BiLSTM/CTC acoustic model is trained and enhanced to improve speech recognition performance. Experimental results show that the proposed methods are suitable for auto speech recognition in radiotelephony communication of civil aviation, and the data enhancement model can effectively reduce the word error rate.