Abstract
We propose two models for verbalizing numbers, a key component in speech recognition and synthesis systems. The first model uses an end-to-end recurrent neural network. The second model, drawing inspiration from the linguistics literature, uses finite-state transducers constructed with a minimal amount of training data. While both models achieve near-perfect performance, the latter model can be trained using several orders of magnitude less data than the former, making it particularly useful for low-resource languages.- Anthology ID:
- Q16-1036
- Volume:
- Transactions of the Association for Computational Linguistics, Volume 4
- Month:
- Year:
- 2016
- Address:
- Cambridge, MA
- Editors:
- Lillian Lee, Mark Johnson, Kristina Toutanova
- Venue:
- TACL
- SIG:
- Publisher:
- MIT Press
- Note:
- Pages:
- 507–519
- Language:
- URL:
- https://aclanthology.org/Q16-1036
- DOI:
- 10.1162/tacl_a_00114
- Cite (ACL):
- Kyle Gorman and Richard Sproat. 2016. Minimally Supervised Number Normalization. Transactions of the Association for Computational Linguistics, 4:507–519.
- Cite (Informal):
- Minimally Supervised Number Normalization (Gorman & Sproat, TACL 2016)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/Q16-1036.pdf