TangLi1996/LiveATC-JP-Corpus: v1.0
Bibliographic record
Abstract
LiveATC-JP-Corpus is a domain-specific corpus of Japanese air traffic control (ATC) communications, designed to support academic research in automatic speech recognition (ASR) and phonetic alignment. The dataset contains 420 audio recordings (approximately 2 hours in total) collected from the RJAA approach frequency via LiveATC.net, with explicit permission for academic use. Each recording is manually transcribed by experienced annotators and aligned at the phoneme level using the Montreal Forced Aligner (MFA), resulting in parallel `.wav`, `.txt`, and `.TextGrid` files. This is the first publicly available corpus focused on Japanese ATC speech, filling a critical gap for empirical evaluation and reproducibility in aviation speech research. It can be used to benchmark ASR systems, study human-ASR collaboration, and analyze the structure of domain-specific spoken communication. The dataset is released under a Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license. The original audio recordings were obtained from LiveATC.net with permission. Transcriptions and alignments were created by the authors for non-commercial academic purposes. Please cite this dataset if used in research.
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.065 | 0.092 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".