metadata

license: cc-by-4.0
datasets:
  - bene-ges/spellmapper_en_train
language:
  - en
library_name: nemo

SpellMapper - Spellchecking ASR Customization Model

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

How to Use this Model

To use this model you will need to install NVIDIA NeMo.

See Bash-script with example of inference pipeline.

Or play with Tutorial.

Citation

    @misc{antonova2023spellmapper,
      title={SpellMapper: A non-autoregressive neural spellchecker for ASR customization with candidate retrieval based on n-gram mappings}, 
      author={Alexandra Antonova and Evelina Bakhturina and Boris Ginsburg},
      year={2023},
      eprint={2306.02317},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
    }