Intelligent air traffic control using NLP-enhanced speech recognition and natural language generation
Why this work is in the frame
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Bibliographic record
Abstract
Abstract The combination of artificial intelligence (AI) and natural language processing (NLP) can bring an intelligent solution to air traffic management (ATM) for reliability, accuracy, and safety. This research aims to present a real-time intelligent system that improves the communication between air traffic controllers (ATCOs) and pilots. The proposed system enhances transcription accuracy, supports automated decision-making, reduces the response time and furthermore improves safety in high-air traffic situations. The proposed architecture is designed to enable scalable integration of AI tools and NLP technologies in ATC systems. The merits of the proposed system is that it automates the whole steps of the communication in the air traffic system, rather than implementing parts of the process. The system begins by automatic speech recognition (ASR) module that is responsible for transforming the speech (instructions) into text. The architecture enables understanding the main ATC instructions via a specific list of most common keywords through a natural language understanding module (NLU), thus enabling the pilot to communicate with the ATM system. Additionally, the system incorporates natural language response generation (NLG) module to reduce pilot workload and improve the communication efficiency. Extensive experiments were conducted for system verification on several datasets, where each dataset is targeted for a specific module of the system. Through the results, the proposed system demonstrates its capability of reducing the communication errors and improving service reliability with the overall word recognition accuracy by the ASR module of 91.73%, while the NLU module gives F1 score to 0.9816, and the NLG module produced acceptable quality of generated texts (85%) and with generation latency ~ 0.6 s. Compared to the recent existing systems, our system gives a better overall performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it