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README.md

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+---
+license: cc0-1.0
+language:
+- ja
+task_categories:
+- automatic-speech-recognition
+- text2text-generation
+tags:
+- asr-error-correction
+- japanese
+- whisper
+- phonetics
+pretty_name: Mondegreen ASR error pairs
+size_categories:
+- 1K<n<10K
+---
+
+# Mondegreen ASR error pairs
+
+`(ASR hypothesis, gold text)` pairs for Japanese ASR post-correction.
+
+This build is **simulated** -- errors come from a phonetic corruption model, not from a real ASR system. It exists so the whole pipeline (gate training, benchmarks, figures, CI) is reproducible without a GPU. Treat every number derived from it as a stated assumption, not a measurement.
+
+## How it was made
+
+```
+synthetic text
+  -> phonetic corruption model (mondegreen.simulate)
+  -> hypothesis
+```
+
+**No audio was involved in this build.** Errors were generated by perturbing the *reading* of each term with the confusion classes the distance function discounts (voicing, long vowels, geminates, moraic nasal) and re-rendering it the way ASR would -- as katakana, or as a homophone kanji spelling drawn from the bundled reading table. The acoustic condition fields below record which condition each record *would* correspond to, and are carried through for parity with the measured pipeline.
+
+To rebuild this dataset with a real TTS -> Whisper round trip:
+
+```bash
+python scripts/harvest_errors.py --mode real --whisper-size small -n 2000
+```
+
+**No LLM judges correctness anywhere in this pipeline.**
+
+- provenance: **simulated**
+- pairs: 9000
+- glossary terms used: 12000
+- acoustic conditions: ['close/15.0', 'close/20.0', 'close/None', 'far/10.0', 'far/12.0', 'reverb/5.0']
+
+## Source text and licence
+
+| field | value |
+| --- | --- |
+| corpus | `synthetic` |
+| licence | CC0-1.0 |
+| verification | Generated by mondegreen.harvest.SentenceFactory; no third-party text. |
+| url | — |
+
+If you add a corpus, add it to `mondegreen.harvest.CORPUS_LICENSES` with a
+verified licence first. The harvester refuses unknown corpora by design.
+
+## Pathology labels
+
+| label | 日本語 | how it is produced |
+| --- | --- | --- |
+| `term-phonetic` | 固有名詞の音韻的置換 | glossary term rendered as a homophone or near-homophone |
+| `voicing` | 濁音・清音の取り違え | rendaku / devoicing slip inside a term |
+| `long-vowel` | 長音の脱落・付加 | chouon added or dropped |
+| `geminate` | 促音の脱落・付加 | sokuon added or dropped |
+| `moraic-nasal` | 撥音の脱落 | moraic nasal swallowed, typically in far-field audio |
+| `particle-drop` | 助詞の欠落 | unstressed particle lost |
+| `word-drop` | 語の脱落 | short span deleted entirely |
+| `number-unit` | 数字・単位の誤り | digit or counter substituted |
+| `hallucination` | 定型の幻聴 | canned phrase emitted over silence or noise-only audio |
+
+Observed counts in this build:
+
+| label | count |
+| --- | --- |
+| `geminate` | 3217 |
+| `hallucination` | 1080 |
+| `long-vowel` | 5688 |
+| `moraic-nasal` | 548 |
+| `number-unit` | 821 |
+| `particle-drop` | 450 |
+| `term-phonetic` | 6093 |
+| `voicing` | 3048 |
+| `word-drop` | 259 |
+
+## Fields
+
+| field | meaning |
+| --- | --- |
+| `id` | stable record id |
+| `gold` | the text that was spoken (exact) |
+| `hypothesis` | what the ASR returned |
+| `glossary_terms` | glossary surfaces occurring in `gold` |
+| `error_types` | pathology labels |
+| `speaker`, `speed`, `snr_db`, `room` | acoustic condition |
+| `asr_model` | which ASR produced the hypothesis |
+| `source_corpus`, `source_license` | provenance of the gold text |
+| `split` | train / test (disjoint speakers, sentences and glossaries) |
+| `provenance` | `measured` (real TTS+ASR) or `simulated` |
+
+## Intended use
+
+Training and evaluating **post-correction** systems. Not for training ASR models.
+
+## Privacy
+
+All names in this dataset are **synthetic**, generated by
+`mondegreen.harvest.GlossaryBuilder`. No real person's voice or name was used, and
+no real meeting audio exists anywhere in this pipeline.