  In this work, we propose a new automatic speech recognition (ASR) system
based on feature learning and an end-to-end training procedure for air traffic
control (ATC) systems. The proposed model integrates the feature learning
block, recurrent neural network (RNN), and connectionist temporal
classification loss to build an end-to-end ASR model. Facing the complex
environments of ATC speech, instead of the handcrafted features, a learning
block is designed to extract informative features from raw waveforms for
acoustic modeling. Both the SincNet and 1D convolution blocks are applied to
process the raw waveforms, whose outputs are concatenated to the RNN layers for
the temporal modeling. Thanks to the ability to learn representations from raw
waveforms, the proposed model can be optimized in a complete end-to-end manner,
i.e., from waveform to text. Finally, the multilingual issue in the ATC domain
is also considered to achieve the ASR task by constructing a combined
vocabulary of Chinese characters and English letters. The proposed approach is
validated on a multilingual real-world corpus (ATCSpeech), and the experimental
results demonstrate that the proposed approach outperforms other baselines,
achieving a 6.9\% character error rate.
