THA: Weighted Finite-State Text Normalization and Inverse Text Normalization for Khmer
Not on the map. Why?
Abstract (arXiv)
Text-to-speech needs written text in spoken form, and speech recognition output needs the reverse. For Khmer, neither direction has a maintained open-source tool, and the script makes both harder: words are not separated by spaces, and number words occur inside ordinary words. We present Tha, a Khmer text normalization and inverse text normalization toolkit built from weighted finite-state transducers. It segments and classifies a whole line in one shortest-path search, and a second transducer rejects token boundaries inside a Khmer syllable. On Google's Khmer test suite, Tha agrees with the reference on all 274 cardinals up to one spelling variant, and on 2,906 real TTS prompts, 153 of the 158 sentences it rewrites are correct. Tha is open source under the Apache 2.0 license.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 7 of its 34 references that are in the map. First posted on arXiv 25 Sep 2026.
Builds on
7 of 34 references.
References · 7
- 2022Robust Speech Recognition via Large-Scale Weak Supervisioncited by 4,057
- 2022FLEURS: Few-shot Learning Evaluation of Universal Representations of Speechcited by 469
- 2016RNN Approaches to Text Normalization: A Challengecited by 30
- 2021NeMo Inverse Text Normalization: From Development To Productioncited by 29
- 2021Neural Inverse Text Normalizationcited by 20
- 2022Shallow Fusion of Weighted Finite-State Transducer and Language Model for Text Normalizationcited by 8
- 2025PolyNorm: Few-Shot LLM-Based Text Normalization for Text-to-Speechcited by 3
Built on by
Not cited yet.