Abstract
The readback errors of radiotelephony communication result in serious potential risk to the air transportation safety. Therefore, it is essential to establish a proper model to identify and also to classify the readback errors automatically so as to improve the flight safety. In this paper, a new scheme, which has two channels to process the instructions and the readbacks (I-R pairs) respectively based on one-layer convolutional neural network (CNN), is proposed for the readback error classification. The semantics of the I-R pairs are learned by the one-layer CNN encoder. Then, the classification decision is made according to a matching vector of the I-R pairs. A new method of input is also tested. Extensive experiments have been conducted and the results show that the proposed scheme is effective for automatic readback error classification and the average classification accuracy on a Chinese civil radiotelephony communication dataset is up to 95.44%.
F. Cheng and G. Jia—These authors contributed equally to this work and should be considered co-first authors.
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Acknowledgements
This work is supported by National Natural Science Foundation of China (No. U1433120, No. 61502498, No. 61379102) and the Fundamental Research Funds for the Central Universities (No. 3122017001).
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Cheng, F., Jia, G., Yang, J., Li, D. (2018). Readback Error Classification of Radiotelephony Communication Based on Convolutional Neural Network. In: Zhou, J., et al. Biometric Recognition. CCBR 2018. Lecture Notes in Computer Science(), vol 10996. Springer, Cham. https://doi.org/10.1007/978-3-319-97909-0_62
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DOI: https://doi.org/10.1007/978-3-319-97909-0_62
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