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# Automatic Generation of English Respellings

**Authors:** Bradley Hauer and Grzegorz Kondrak · **Year:** 2013 · **Venue:** Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT 2013), pages 634–643 **Link:** [aclanthology.org/N13-1072](https://aclanthology.org/N13-1072/) · **Licence:** [CC BY-NC-SA 3.0](https://creativecommons.org/licenses/by-nc-sa/3.0/) (ACL Anthology, before 2016) — summary only; the Web-derived respelling corpus and the test word lists are for evaluation only

[**2** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/hauer-2013-automatic-generation-english-respellings/) of the 140673 papers whose bibliographies we read[Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies](https://aclanthology.org/volumes/N13-1/)[licence](https://creativecommons.org/licenses/by-nc-sa/3.0/)

- [Publisher's page](https://aclanthology.org/N13-1072/)
- [PDF](https://aclanthology.org/N13-1072.pdf)

**Authors (2)**

1. Bradley Hauer
2. Grzegorz Kondrak

Citations and author metrics from OpenAlex (); authors with the same name are not always told apart.

## What it is about

A respelling is an alternative spelling that tells a reader how to pronounce a word, as in "wah-tool-koh". The task is to generate one from a phoneme sequence. The system overgenerates syllabifications, filtered by sonority and lax-vowel constraints that are relaxed in a fixed order when nothing survives. Candidate syllables come from a phoneme-to-letter model trained on 4,215 monosyllabic words and from a hand-written context-sensitive respeller. A letter-to-phoneme model rejects candidates that do not read back correctly. A vowel counter and an SVM over letter n-grams (1 to 5) scored the ambiguity of each syllable, and a dictionary reviser can swap in a real word. On 231 words from the Web, the eSpeak synthesiser reads the generated respellings with 93.0% phoneme accuracy, against 76.2% for the original spelling. In a 20-judge study, 70.0% of the system's respellings were judged unambiguous, and 41.3% unambiguous and correct, against 32.6% for the Web respellings and 25.5% for the baseline. An ablation shows that removing the letter-to-phoneme filter almost doubles the phoneme error rate.

## Ideas

- I1. [05. The writing system](https://md.mova.elamur.ai/free/en/docs/languages/en/05-writing-system/): a closed table of respelling rules per phoneme (the context-free mapping, plus the context-sensitive variants) as explicit rules with their reason, so each respelling can say which rule produced each syllable.
- I2. The ambiguity score as a count, not an SVM: for each letter n-gram in a respelling, count how many pronunciations it has in a dictionary, and reject a syllable whose n-grams have more than one reading. Deterministic and explainable; measure its agreement with the judges' decisions on the paper's examples.
- I3. The round-trip check as a negative control for spelling output: a generated spelling must read back to the same sound through a letter-to-phoneme rule set, and the rate of failures must be reported with the set of failing rules.
- I4. Constraint relaxation in a fixed order (sonority, lax vowels in open syllables) as an explicit schedule in code, with the relaxation step recorded per output.

## For Mova

The chapters are [05. The writing system](https://md.mova.elamur.ai/free/en/docs/languages/en/05-writing-system/) and [06. Spelling rules: inflection, derivation, compounds and national standards](https://md.mova.elamur.ai/free/en/docs/languages/en/06-spelling-rules/). The code points are `en/src/tok.rs` (syllable boundaries if they are added) and `en/src/morph.rs` (rule tables). Nothing here is a phonology module yet, so the first step is a data question: a pronunciation dictionary whose licence allows use. The measure: round-trip accuracy by rule on a held-out word list, plus the ablation of the reading filter, run twice for determinism.

## Cite

The paper itself, not this card. BibTeX from the library's record of the work.

```
@inproceedings{hauer2013automatic,
  title = {{Automatic Generation of English Respellings}},
  author = {Hauer, Bradley and Kondrak, Grzegorz},
  booktitle = {Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies},
  year = {2013},
  url = {https://aclanthology.org/N13-1072/}
}
```

- [← previous Combining Event Semantics and Degree Semantics for Natural Language Inference](https://md.mova.elamur.ai/free/en/docs/papers/haruta-2020-combining-event-semantics-degree-semantics/)
- [next → Question-Answer Driven Semantic Role Labeling: Using Natural Language to Annotate Natural Language](https://md.mova.elamur.ai/free/en/docs/papers/he-2015-question-answer-driven-semantic-role/)
- [↑ back to English](https://md.mova.elamur.ai/free/en/docs/papers/en/)

## References

19 references, 0 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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