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