Adapting Automatic Speech Recognition for Accented Air Traffic Control
  Communications
Authors: name: Marcus Yu Zhe Wee, name: Justin Juin Hng Wong, name: Lynus Lim, name: Joe Yu Wei Tan, name: Prannaya Gupta, name: Dillion Lim, name: En Hao Tew, name: Aloysius Keng Siew Han, name: Yong Zhi Lim

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.