Automatic Speech Recognition (ASR), as the assistance of speech communication
between pilots and air-traffic controllers, can significantly reduce the
complexity of the task and increase the reliability of transmitted information.
ASR application can lead to a lower number of incidents caused by
misunderstanding and improve air traffic management (ATM) efficiency.
Evidently, high accuracy predictions, especially, of key information, i.e.,
callsigns and commands, are required to minimize the risk of errors. We prove
that combining the benefits of ASR and Natural Language Processing (NLP)
methods to make use of surveillance data (i.e. additional modality) helps to
considerably improve the recognition of callsigns (named entity). In this
paper, we investigate a two-step callsign boosting approach: (1) at the 1 step
(ASR), weights of probable callsign n-grams are reduced in G.fst and/or in the
decoding FST (lattices), (2) at the 2 step (NLP), callsigns extracted from the
improved recognition outputs with Named Entity Recognition (NER) are correlated
with the surveillance data to select the most suitable one. Boosting callsign
n-grams with the combination of ASR and NLP methods eventually leads up to
53.7% of an absolute, or 60.4% of a relative, improvement in callsign
recognition.

本文通过将 ASR 和 NLP 方法相结合并利用监控数据，研究了一种两步增强 callsign 的方法，最终提高了 53.7% 的绝对召唤符号识别率和 60.4% 的相对召唤符号识别率。

利用语境数据的两步方法：空中交通通信中的语音识别

A two-step approach to leverage contextual data: speech recognition in air-traffic communications

Updated on 24/09/2015: This update provides preliminary experiment results
for fine-grained classification on the surveillance data of CompCars. The
train/test splits are provided in the updated dataset. See details in Section
6.

该研究提供了针对 CompCars 监控数据的细粒度分类的初步实验结果，同时提供了更新后的数据集的训练 / 测试拆分，详见第 6 节。