| --- |
| license: cc0-1.0 |
| language: |
| - ja |
| task_categories: |
| - automatic-speech-recognition |
| - text-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. |
|
|
