Automatic speech recognition and chat bot for air traffic control
Artificial Intelligence (AI) has demonstrated the ability to manage complex processes highly effectively and thus is widely seen as a key component in future airport ATM systems. Future AI tools for ATMs will rely on digital data, such as surveillance, radar, weather, and flight plans, for the...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2024
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| Online Access: | https://hdl.handle.net/10356/177842 |
| _version_ | 1826120737455341568 |
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| author | Low, Ashton Kin Yun |
| author2 | Sameer Alam |
| author_facet | Sameer Alam Low, Ashton Kin Yun |
| author_sort | Low, Ashton Kin Yun |
| collection | NTU |
| description | Artificial Intelligence (AI) has demonstrated the ability to manage complex processes
highly effectively and thus is widely seen as a key component in future airport ATM
systems. Future AI tools for ATMs will rely on digital data, such as surveillance, radar,
weather, and flight plans, for their operation. However, the foundational Air Traffic
Control Officer (ATCo)-pilot communication medium is voice, which is a vital source
of situational data. Controller Pilot Data Link Communications (CPDLC) has been
developed as an alternative, text-based communication delivery method, however,
ATCo-pilot communications will not be completely transitioned to this framework in
the near-term future. Moreover, as CPDLC is a one-to-one communication paradigm,
the additional situational awareness of other traffic provided by traditional party-line
VHF communications is potentially lost. Therefore, an automated speech-to-text
translation tool can be seen as a missing link, enabling traditional ATCo-pilot voice
communications to be automatically translated and input into a datalink system such
as CPDLC. To this end this paper presents a Machine Learning (ML) based Automatic
Speech Recognition (ASR) framework that is able to accurately translate VHF-quality
ATCo-pilot speech communication to text, achieving a Word Error Rate of only
6.13%. Moreover, the presented model is able to extract crucial information with an
accuracy and F1-score of 95.2% and 90.5% respectively. A detailed design of the
framework is provided to enable its replication by the wider research community. |
| first_indexed | 2024-10-01T05:21:21Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/177842 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:21:21Z |
| publishDate | 2024 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1778422024-05-31T12:34:43Z Automatic speech recognition and chat bot for air traffic control Low, Ashton Kin Yun Sameer Alam School of Mechanical and Aerospace Engineering sameeralam@ntu.edu.sg Engineering Automatic speech recognition Artificial Intelligence (AI) has demonstrated the ability to manage complex processes highly effectively and thus is widely seen as a key component in future airport ATM systems. Future AI tools for ATMs will rely on digital data, such as surveillance, radar, weather, and flight plans, for their operation. However, the foundational Air Traffic Control Officer (ATCo)-pilot communication medium is voice, which is a vital source of situational data. Controller Pilot Data Link Communications (CPDLC) has been developed as an alternative, text-based communication delivery method, however, ATCo-pilot communications will not be completely transitioned to this framework in the near-term future. Moreover, as CPDLC is a one-to-one communication paradigm, the additional situational awareness of other traffic provided by traditional party-line VHF communications is potentially lost. Therefore, an automated speech-to-text translation tool can be seen as a missing link, enabling traditional ATCo-pilot voice communications to be automatically translated and input into a datalink system such as CPDLC. To this end this paper presents a Machine Learning (ML) based Automatic Speech Recognition (ASR) framework that is able to accurately translate VHF-quality ATCo-pilot speech communication to text, achieving a Word Error Rate of only 6.13%. Moreover, the presented model is able to extract crucial information with an accuracy and F1-score of 95.2% and 90.5% respectively. A detailed design of the framework is provided to enable its replication by the wider research community. Bachelor's degree 2024-05-31T12:34:42Z 2024-05-31T12:34:42Z 2024 Final Year Project (FYP) Low, A. K. Y. (2024). Automatic speech recognition and chat bot for air traffic control. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/177842 https://hdl.handle.net/10356/177842 en application/pdf Nanyang Technological University |
| spellingShingle | Engineering Automatic speech recognition Low, Ashton Kin Yun Automatic speech recognition and chat bot for air traffic control |
| title | Automatic speech recognition and chat bot for air traffic control |
| title_full | Automatic speech recognition and chat bot for air traffic control |
| title_fullStr | Automatic speech recognition and chat bot for air traffic control |
| title_full_unstemmed | Automatic speech recognition and chat bot for air traffic control |
| title_short | Automatic speech recognition and chat bot for air traffic control |
| title_sort | automatic speech recognition and chat bot for air traffic control |
| topic | Engineering Automatic speech recognition |
| url | https://hdl.handle.net/10356/177842 |
| work_keys_str_mv | AT lowashtonkinyun automaticspeechrecognitionandchatbotforairtrafficcontrol |