Modeling Mitigations and Hazards in UAS Emergency Response Operations
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In recent years, the application of automatic speech recognition has gained popularity across diverse industries, including aviation. Given the many applications focusing on transcribing air traffic control and management communication, this paper explores the training of OpenAI's Whisper model across multiple existing public and private air traffic control voice datasets in an effort to improve robustness. Combining roughly 60+ hours of various air traffic datasets, our goal is to train a unified Whisper model and expect an average word error rate reduction across testing datasets. Furthermore, this work aims to understand the data quantity requirements for achieving state-of-the art results by comprehensively training Whisper on varying dataset sizes. This work has the potential to improve automatic speech recognition performance across the domain, improve understanding of the quantity of data required by an aviation speech recognition system, and lastly provide metrics to compare and improve upon in future research.
Emerging aviation includes the use of small Unmanned Aerial Systems (UAS) in novel operations. The manufacture and operation of these small UAS are not as regulated as today’s commercial operation, and their widespread use introduces new risks and hazards to the general public. Today, there are case-by-case approvals for sUAS operations, particularly for emergency response operations in which the potential benefits to use of sUAS is perceived to outweigh potential risks. We analyze operational approvals, procedures, and concepts of operation to identify and categorize the risks and hazards that applicants and approvers are already considering, and also identify barriers and mitigations that the operators have already put in place. This analysis may help lead to routine checklists that standardize safety analysis and lead to more routine operations.
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