Adapting Automatic Speech Recognition for Accented Air Traffic Control
  Communications
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.
67c11d0bd1f37121ad63acfb
Marcus Yu Zhe Wee
67c11d0bd1f37121ad63acfc
Justin Juin Hng Wong
claimed_verified
2025-02-28T12:15:29.818Z
63b9189c060d6595d2af72a8
/avatars/dff8c3f215a2e5192baec752c34c5ed0.svg
Justin Wong
user
amidstdebug
67c11d0bd1f37121ad63acfd
Lynus Lim
67c11d0bd1f37121ad63acfe
Joe Yu Wei Tan
67c11d0bd1f37121ad63acff
Prannaya Gupta
claimed_verified
2025-02-28T12:15:32.107Z
60a546bdf9b53404e7806278
https://cdn-avatars.huggingface.co/v1/production/uploads/1621444268349-noauth.png
Prannaya Gupta
user
ThePyProgrammer
67c11d0bd1f37121ad63ad00
Dillion Lim
67c11d0bd1f37121ad63ad01
En Hao Tew
67c11d0bd1f37121ad63ad02
Aloysius Keng Siew Han
67c11d0bd1f37121ad63ad03
Yong Zhi Lim
Adapting Automatic Speech Recognition for Accented Air Traffic Control
  Communications
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.
ASR models fine-tuned for Southeast Asian accents using a new dataset achieve significant improvements in transcription accuracy in noisy ATC environments.
Automatic Speech Recognition (ASR) Word Error Rate (WER) Southeast Asian accents noise-robust training region-specific datasets