Operational machine-learning based assistant systems must be robust in a wide range of scenarios. This hold especially true for the air-traffic control (ATC) domain. The robustness of an architecture is particularly evident in edge cases, such as high word error rate (WER) transcripts resulting from noisy ATC recordings or partial transcripts due to clipped recordings. To increase the edge-case robustness of call-sign recognition and understanding (CRU), a core tasks in ATC speech processing, we propose the multimodal call-sign-command recovery model (CCR). The CCR architecture leads to an increase in the edge case performance of up to 15%. We demonstrate this on our second proposed architecture, CallSBERT. A CRU model that has less parameters, can be fine-tuned noticeably faster and is more robust during fine-tuning than the state of the art for CRU. Furthermore, we demonstrate that optimizing for edge cases leads to a significantly higher accuracy across a wide operational range.

本研究解决了在高噪声环境下空中交通管制领域呼号识别（CRU）的鲁棒性问题，提出了一种多模态呼号命令恢复模型（CCR），使边缘案例的表现提高了15%。研究表明，通过针对边缘案例进行优化，可以显著提高模型在广泛操作范围内的准确性。

利用多模态数据增强边缘案例的鲁棒呼号识别与理解