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---
license: cc-by-4.0
datasets:
- bene-ges/spellmapper_en_train_v1
language:
- en
library_name: nemo
---
# SpellMapper - Spellchecking ASR Customization Model
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This model is an alternative to word boosting/shallow fusion approaches:
- does not require retraining ASR model;
- does not require beam-search/language model (LM);
- can be applied on top of any English ASR model output;
Paper: [SpellMapper: A non-autoregressive neural spellchecker for ASR customization with candidate retrieval based on n-gram mappings](https://arxiv.org/abs/2306.02317)
[Documentation page](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/nlp/spellchecking_asr_customization.html).
## How to Use this Model
To use this model you will need to install [NVIDIA NeMo](https://github.com/NVIDIA/NeMo).
See [Bash-script](https://github.com/NVIDIA/NeMo/blob/main/examples/nlp/spellchecking_asr_customization/run_infer.sh) with example of inference pipeline.
Or play with [Tutorial](https://github.com/NVIDIA/NeMo/blob/main/tutorials/nlp/SpellMapper_English_ASR_Customization.ipynb).
## Citation
```bibtex
@inproceedings{inproceedings,
author = {Antonova, Alexandra and Bakhturina, Evelina and Ginsburg, Boris},
year = {2023},
month = {08},
pages = {1404-1408},
title = {SpellMapper: A non-autoregressive neural spellchecker for ASR customization with candidate retrieval based on n-gram mappings},
doi = {10.21437/Interspeech.2023-768}
}
```
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