適應自動語音辨識技術於帶口音的空中交通管制通訊

Adapting Automatic Speech Recognition for Accented Air Traffic Control Communications

February 27, 2025

作者: Marcus Yu Zhe Wee, Justin Juin Hng Wong, Lynus Lim, Joe Yu Wei Tan, Prannaya Gupta, Dillion Lim, En Hao Tew, Aloysius Keng Siew Han, Yong Zhi Lim

cs.AI

摘要

在航空交通管制（ATC）中，有效的溝通對於維持航空安全至關重要，然而，帶有口音的英語在自動語音識別（ASR）系統中所帶來的挑戰，至今仍大多未獲解決。現有的模型在處理東南亞口音（SEA-accented）語音，尤其是在噪音環境下的ATC語音時，轉錄準確度面臨困難。本研究展示了利用新創建的數據集，針對東南亞口音進行微調的ASR模型開發。我們的研究取得了顯著改進，在東南亞口音的ATC語音上達到了0.0982或9.82%的詞錯誤率（WER）。此外，本文強調了地區特定數據集和以口音為重點的培訓的重要性，為在資源有限的軍事行動中部署ASR系統提供了途徑。研究結果強調了需要噪音魯棒的培訓技術和地區特定數據集，以提高非西方口音在ATC通信中的轉錄準確度。

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.