| --- |
| license: cc-by-4.0 |
| language: |
| - en |
| - he |
| pretty_name: ASR Error Prediction (Hebrish) |
| tags: |
| - asr |
| - speech-recognition |
| - error-prediction |
| - code-switching |
| - fine-tuning |
| - hebrish |
| task_categories: |
| - text-classification |
| size_categories: |
| - 1K<n<10K |
| configs: |
| - config_name: fixes |
| data_files: |
| - split: codeswitching |
| path: fixes/codeswitching.jsonl |
| - split: tech |
| path: fixes/tech.jsonl |
| - split: names |
| path: fixes/names.jsonl |
| - split: general_english |
| path: fixes/general_english.jsonl |
| - config_name: candidates |
| data_files: |
| - split: codeswitching |
| path: candidates/codeswitching.jsonl |
| - split: tech |
| path: candidates/tech.jsonl |
| - config_name: predictions-haiku-r1 |
| data_files: |
| - split: codeswitching |
| path: predictions/haiku-r1/codeswitching.jsonl |
| - split: tech |
| path: predictions/haiku-r1/tech.jsonl |
| - split: general_english |
| path: predictions/haiku-r1/general_english.jsonl |
| --- |
| |
| # ASR Error Prediction |
|
|
| Training data for a model that predicts **which words a speech recognition model will get wrong for |
| one speaker**, by extrapolating from the words it has already been confirmed to get wrong. Built for |
| the [Hebrish](https://hebrish.app) fine-tunes of `nvidia/parakeet-tdt-0.6b-v2` (English with |
| romanized Hebrew code-switching). Plan, scripts and method: |
| [danielrosehill/ASR-Mistranscription-Predictor](https://github.com/danielrosehill/ASR-Mistranscription-Predictor). |
|
|
| ## Segments |
|
|
| Every config is split by **segment**, so predictions can be made and used per training dataset: |
|
|
| | Segment | What it covers | Source of candidates | |
| |---|---|---| |
| | `codeswitching` | romanized Hebrew and Yiddish terms | the Hebrish lexicon (places, food, bureaucracy, religious life, ...) | |
| | `tech` | tech, AI, Linux and smart-home terms and acronyms | the project's tech vocabulary packs, plus LLM proposals | |
| | `names` | people, companies, projects | none yet (fixes only) | |
| | `general_english` | ordinary English words | LLM proposals | |
|
|
| | Config | codeswitching | tech | names | general_english | |
| |---|---|---|---|---| |
| | `fixes` | 7 | 59 | 9 | 12 | |
| | `candidates` | 3634 | 1112 | 0 | 0 | |
| | `predictions-haiku-r1` | 3634 | 1163 | 0 | 55 | |
| |
| ## Configs |
| |
| - **`fixes`**: the positives. Words confirmed wrong in the speaker's real dictation, from |
| [`Hebrish/ASR-Error-List`](https://huggingface.co/datasets/Hebrish/ASR-Error-List) (pinned in |
| `error_list_rev`): `word`, `heard_as` (the wrong forms the ASR wrote), `kinds`, `occurrences`, |
| `models`, dates, and `label` (`acoustic`, or `orthographic` when the only error is casing or a |
| hyphen). |
| - **`candidates`**: words the speaker is likely to say that are not on the fix list: `term`, |
| `domain`, `meaning`, `takes` (recorded training takes so far; null = unknown) and `pool` (source |
| file and commit). |
| - **`predictions-<run>`**: one model's scores for the candidates, plus terms it proposed outside the |
| pools (`origin`: `pool` or `proposed`): `p_fail` (0-100), `pattern` (the confusion class it |
| applied), `likely_heard_as`, `reason`, `rank` within the segment. |
|
|
| **Predictions are not labels.** They are rankings to be checked against the real model (a synthetic |
| probe, then a few real recordings) before any word gets training sentences. Confirmed ones move to |
| the error list with `source: predicted`. |
|
|
| ## Runs |
|
|
| | Run | Date | Model | Notes | |
| |---|---|---|---| |
| | `haiku-r1` | 2026-10-25 | `claude-haiku-5-5` (Anthropic Batch API, low effort; batch `msgbatch_01SFKjYbEtvGqChr8eo3JXPk`, US$0.13) | first pass; fix list at 87 words; scores every candidate in chunks of 60 with the whole fix list as evidence, plus 52 proposed tech terms and 55 proposed English words; 1 candidate missing from the replies | |
|
|
| **`haiku-r1` caveats.** Scores are uncalibrated and not comparable across segments. Code-switching |
| scores run high (mean 58 for terms with no recorded takes, 37% of candidates at 60 or above) because |
| the fix list has only 7 Hebrew words to learn from, so Haiku leans on "Hebrew = hard". Scores do |
| fall with recorded takes (mean 58 at 0 takes, 49 at 1 to 4, 31 at 5 to 19, 22 at 20 or more), which |
| is the expected direction. Use ranks within a segment, not thresholds. Wrong forms that equalled |
| the term itself (1,175 of them) were removed after the run. |
|
|
| ## Not included |
|
|
| No audio, and nothing from the speaker's dictations beyond single confirmed words and the ASR's |
| wrong forms. Negatives (rare words the model transcribed correctly) are planned but not published |
| yet. |
|
|
