ATCCaps is a call-sign-aware ATC speech dataset containing 202.94 hours of audio, 170385 utterances and 922 unique call signs, constructed via transcript parsing, ADS-B metadata, normalization, filtering and LLM captioning.
ATCO2 corpus: A large-scale dataset for research on au- tomatic speech recognition and natural language understand- ing of air traffic control communications
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BEARD adapts Whisper encoder for ATC domain via BEST-RQ and distillation on 5000h unlabeled speech then 2h labeled fine-tuning, delivering 12% relative WER gain over fine-tuned baseline.
SCOPE achieves 91.05% open-set detection accuracy and corrects 96.63% of anomalous ATC readbacks via frozen LLM with plug-in classifier and in-context learning on semi-synthetic data.
ASTRA automates simpilot roles in ATCO training with a fine-tuned ASR pipeline that cuts WER to 23.45% on Singaporean aviation speech and an AI evaluator scoring 86.9-91.7% on accuracy, brevity, and completeness.
citing papers explorer
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ATCCaps: A Call-Sign-Aware Speech Dataset for Air Traffic Control Recognition
ATCCaps is a call-sign-aware ATC speech dataset containing 202.94 hours of audio, 170385 utterances and 922 unique call signs, constructed via transcript parsing, ADS-B metadata, normalization, filtering and LLM captioning.
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BEST-RQ-Based Self-Supervised Learning for Whisper Domain Adaptation
BEARD adapts Whisper encoder for ATC domain via BEST-RQ and distillation on 5000h unlabeled speech then 2h labeled fine-tuning, delivering 12% relative WER gain over fine-tuned baseline.
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SCOPE: A Lightweight-training LLM Framework for Air Traffic Control Readback Monitoring
SCOPE achieves 91.05% open-set detection accuracy and corrects 96.63% of anomalous ATC readbacks via frozen LLM with plug-in classifier and in-context learning on semi-synthetic data.
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ASTRA: A Scalable Next-Generation ATCO Training Simulator with Autonomous Simpilots
ASTRA automates simpilot roles in ATCO training with a fine-tuned ASR pipeline that cuts WER to 23.45% on Singaporean aviation speech and an AI evaluator scoring 86.9-91.7% on accuracy, brevity, and completeness.