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NagaYu/mondegreen-asr-errors

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) ->… See the full description on the dataset page: https://huggingface.co/datasets/NagaYu/mondegreen-asr-errors.

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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.