适应口音差异的空中交通管制通信自动语音识别

摘要

空中交通管制（ATC）中的有效沟通对保障航空安全至关重要，然而带有口音的英语在自动语音识别（ASR）系统中带来的挑战仍未得到充分解决。现有模型在转录东南亚口音（SEA-accented）语音时，尤其是在嘈杂的ATC环境中，表现欠佳。本研究通过使用新构建的数据集，开发了专门针对东南亚口音进行微调的ASR模型。我们的研究取得了显著进展，在东南亚口音的ATC语音上实现了0.0982（即9.82%）的词错误率（WER）。此外，本文强调了区域特定数据集和以口音为重点的训练的重要性，为在资源受限的军事行动中部署ASR系统提供了路径。研究结果强调了采用抗噪训练技术和区域特定数据集以提高非西方口音在ATC通信中转录准确性的必要性。

English

Effective communication in Air Traffic Control (ATC) is critical to maintaining aviation safety, yet the challenges posed by accented English remain largely unaddressed in Automatic Speech Recognition (ASR) systems. Existing models struggle with transcription accuracy for Southeast Asian-accented (SEA-accented) speech, particularly in noisy ATC environments. This study presents the development of ASR models fine-tuned specifically for Southeast Asian accents using a newly created dataset. Our research achieves significant improvements, achieving a Word Error Rate (WER) of 0.0982 or 9.82% on SEA-accented ATC speech. Additionally, the paper highlights the importance of region-specific datasets and accent-focused training, offering a pathway for deploying ASR systems in resource-constrained military operations. The findings emphasize the need for noise-robust training techniques and region-specific datasets to improve transcription accuracy for non-Western accents in ATC communications.

适应口音差异的空中交通管制通信自动语音识别

Adapting Automatic Speech Recognition for Accented Air Traffic Control Communications

摘要

Summary

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