Adapting Automatic Speech Recognition for Accented Air Traffic Control Communications
This addresses a critical safety issue in aviation for non-Western accents, though it is incremental as it adapts existing methods to a specific domain.
The study tackled the problem of low transcription accuracy for Southeast Asian-accented English in noisy Air Traffic Control communications by fine-tuning Automatic Speech Recognition models, achieving a Word Error Rate of 9.82%.
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.