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Prediction and extraction of tower controller commands for speech recognition applications
Journal of Air Transport Management ( IF 5.428 ) Pub Date : 2021-06-07 , DOI: 10.1016/j.jairtraman.2021.102089
Oliver Ohneiser , Hartmut Helmke , Shruthi Shetty , Matthias Kleinert , Heiko Ehr , Šarūnas Murauskas , Tomas Pagirys

Air traffic controllers' (ATCos) workload often is a limiting factor for air traffic capacity. Thus, electronic support systems intend to reduce ATCos' workload. Automatic speech recognition can extract controller command elements from verbal clearances to deliver automatic input for air traffic control systems, thereby avoiding manual input. Assistant Based Speech Recognition (ABSR) with high command recognition rates and low error rates has proven to dramatically reduce ATCos’ workload and increase capacity in approach scenarios. However, ABSR needs accurate hypotheses on expected commands and accurate extractions of command annotations from utterance transcriptions to achieve the required performance. Based on the experience of implementation for approach control, a hypotheses generator and a command extractor have been developed for speech recognition applications regarding tower control communication to face current and future challenges in the aerodrome environment. Three human-in-the-loop multiple remote tower simulation studies were performed with 16 ATCos from Hungary, Lithuania, and Finland at DLR Braunschweig from 2017 to 2019. Roughly 100 h of speech with corresponding radar data were recorded. Around 6000 speech utterances resulting in 16,000 commands have been manually transcribed and annotated. Some parts of the data have been used for training prediction models and command extraction algorithms. Other parts were used for evaluation of command prediction and command extraction. The automatic command extractor achieved a command extraction rate of 96.7%. The hypotheses generator showed operational feasibility with a sufficiently low command prediction error rate of 7.3%.



中文翻译:

用于语音识别应用的塔台控制器命令的预测和提取

空中交通管制员 (ATCos) 的工作量通常是空中交通容量的限制因素。因此,电子支持系统旨在减少空中交通管制员的工作量。自动语音识别可以从口头许可中提取管制员命令要素,为空中交通管制系统提供自动输入,从而避免手动输入。具有高指令识别率和低错误率的辅助语音识别 (ABSR) 已被证明可以显着减少 ATCos 的工作量并增加进场场景中的容量。然而,ABSR 需要对预期命令的准确假设和从话语转录中准确提取命令注释来实现所需的性能。根据进近管制的实施经验,已经开发了一个假设生成器和一个命令提取器,用于有关塔台控制通信的语音识别应用,以应对机场环境中当前和未来的挑战。2017 年至 2019 年,在 DLR Braunschweig 对来自匈牙利、立陶宛和芬兰的 16 个 ATCo 进行了三项人在回路多远程塔模拟研究。 记录了大约 100 小时的语音和相应的雷达数据。人工转录和注释了大约 6000 条语音,产生了 16,000 个命令。部分数据已用于训练预测模型和命令提取算法。其他部分用于评估命令预测和命令提取。自动命令提取器实现了96.7%的命令提取率。

更新日期:2021-06-07
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