Semantic consistency verification of radiotelephony communication based on deep CNN

Abstract

Abstract: The accuracy of readback plays an important role in flight safety. However, readback semantic inconsistency may occurs during radiotelephony communication. Thus, a method based on deep CNN (convolutional neural network) is proposed to verify semantic consistency of readback content. Words in radiotelephony communication corpus are transformed into word vectors. On top of the word vectors, CNN model is used to obtain the semantic vectors of readback sentence pairs, then cosine measurement is applied to measure the semantic similarity of readback sentence pairs. Finally, consistency verification is implemented by classifier. Experiment results show that semantic consistency of radiotelephony communication can be verified by CNN model effectively. The average accuracy of the experiments is up to 82.5%.

Key words: radiotelephony communication, deep CNN, semantic consistency verification, semantic vector

CLC Number:

V355
TP391

YANG Jinfeng, LU Weibing, JIA Guimin, SHI Yihua. Semantic consistency verification of radiotelephony communication based on deep CNN[J]. .

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