Adapting Automatic Speech Recognition for Accented Air Traffic Control Communications
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arXiv
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| Autores principales: | , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866929734629195776 |
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| author | Wee, Marcus Yu Zhe Wong, Justin Juin Hng Lim, Lynus Tan, Joe Yu Wei Gupta, Prannaya Lim, Dillion Tew, En Hao Han, Aloysius Keng Siew Lim, Yong Zhi |
| author_facet | Wee, Marcus Yu Zhe Wong, Justin Juin Hng Lim, Lynus Tan, Joe Yu Wei Gupta, Prannaya Lim, Dillion Tew, En Hao Han, Aloysius Keng Siew Lim, Yong Zhi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_20311 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Adapting Automatic Speech Recognition for Accented Air Traffic Control Communications Wee, Marcus Yu Zhe Wong, Justin Juin Hng Lim, Lynus Tan, Joe Yu Wei Gupta, Prannaya Lim, Dillion Tew, En Hao Han, Aloysius Keng Siew Lim, Yong Zhi Machine Learning Sound Audio and Speech Processing 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. |
| title | Adapting Automatic Speech Recognition for Accented Air Traffic Control Communications |
| topic | Machine Learning Sound Audio and Speech Processing |
| url | https://arxiv.org/abs/2502.20311 |