# NeMo Inverse Text Normalization: From Development To Production

arXiv: https://arxiv.org/abs/2104.05055 · HTML: https://mlsanity.com/paper/2104.05055/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 16 references that are in the map.
- First posted on arXiv 11 Apr 2021.

## Builds on (most cited first, 3 of 3)

- [NeMo: a toolkit for building AI applications using Neural Modules](https://mlsanity.com/paper/1909.09577/) (2019)
- [Neural Inverse Text Normalization](https://mlsanity.com/paper/2102.06380/) (2021)
- [Text normalization using memory augmented neural networks](https://mlsanity.com/paper/1806.00044/) (2018)

## Built on by (most cited first, 29 of 29)

- [DiTTo-TTS: Diffusion Transformers for Scalable Text-to-Speech without Domain-Specific Factors](https://mlsanity.com/paper/2406.11427/) (2024)
- [A Chat About Boring Problems: Studying GPT-based text normalization](https://mlsanity.com/paper/2309.13426/) (2023)
- [On the Effects of Heterogeneous Data Sources on Speech-to-Text Foundation Models](https://mlsanity.com/paper/2406.09282/) (2024)
- [1000 African Voices: Advancing inclusive multi-speaker multi-accent speech synthesis](https://mlsanity.com/paper/2406.11727/) (2024)
- [The Interspeech 2025 Speech Accessibility Project Challenge](https://mlsanity.com/paper/2507.22047/) (2025)
- [Shallow Fusion of Weighted Finite-State Transducer and Language Model for Text Normalization](https://mlsanity.com/paper/2203.15917/) (2022)
- [Fast Context-Biasing for CTC and Transducer ASR models with CTC-based Word Spotter](https://mlsanity.com/paper/2406.07096/) (2024)
- [A Unified Transformer-based Framework for Duplex Text Normalization](https://mlsanity.com/paper/2108.09889/) (2021)
- [SpeechWeave: Diverse Multilingual Synthetic Text & Audio Data Generation Pipeline for Training Text to Speech Models](https://mlsanity.com/paper/2509.14270/) (2025)
- [indic-punct: An automatic punctuation restoration and inverse text normalization framework for Indic languages](https://mlsanity.com/paper/2203.16825/) (2022)
- [Are E2E ASR models ready for an industrial usage?](https://mlsanity.com/paper/2112.12572/) (2021)
- [SupertonicTTS: Towards Highly Efficient and Streamlined Text-to-Speech System](https://mlsanity.com/paper/2503.23108/) (2025)
- [Dynamic Context-Aware Streaming Pretrained Language Model For Inverse Text Normalization](https://mlsanity.com/paper/2505.24229/) (2025)
- [Length-Aware Rotary Position Embedding for Text-Speech Alignment](https://mlsanity.com/paper/2509.11084/) (2025)
- [Open Automatic Speech Recognition Models for Classical and Modern Standard Arabic](https://mlsanity.com/paper/2507.13977/) (2025)
- [The Speech-LLM Takes It All: A Truly Fully End-to-End Spoken Dialogue State Tracking Approach](https://mlsanity.com/paper/2510.09424/) (2025)
- [ReHear: Iterative Pseudo-Label Refinement for Semi-Supervised Speech Recognition via Audio Large Language Models](https://mlsanity.com/paper/2602.18721/) (2026)
- [SpokenUS: A Spoken User Simulator for Task-Oriented Dialogue](https://mlsanity.com/paper/2603.16783/) (2026)
- [OxfordVGG Submission to the EGO4D AV Transcription Challenge](https://mlsanity.com/paper/2307.09006/) (2023)
- [Improving Robustness of Neural Inverse Text Normalization via Data-Augmentation, Semi-Supervised Learning, and Post-Aligning Method](https://mlsanity.com/paper/2309.08626/) (2023)
- [Positional Description for Numerical Normalization](https://mlsanity.com/paper/2408.12430/) (2024)
- [Sometin Beta Pass Notin: Improving Multilingual ASR for Nigerian Languages via Knowledge Distillation](https://mlsanity.com/paper/2605.17710/) (2026)
- [Earnings25: A Comprehensive 500-Hour Speech Benchmark for Finance](https://mlsanity.com/paper/2607.23813/) (2026)
- [Dual-Form ASR: Semantics-Aware Inverse Text Normalization for Chinese Speech Recognition](https://mlsanity.com/paper/2609.02901/) (2026)
- [The Hidden Cost of Digits: Number Normalization and WER in ASR Systems](https://mlsanity.com/paper/2609.21084/) (2026)
- [THA: Weighted Finite-State Text Normalization and Inverse Text Normalization for Khmer](https://mlsanity.com/paper/2609.30984/) (2026)
- [Context Spanning: A Communication Framework for Full-Duplex Speech Models and External LLM Backends](https://mlsanity.com/paper/2609.33443/) (2026)
- [FuseAlign: Forced Alignment in the Wild](https://mlsanity.com/paper/2609.33650/) (2026)
- [Towards Efficient Simultaneous Inverse Text Normalization with Pretrained Text-to-Text Language Model and Read-Tag-Write Policy](https://mlsanity.com/paper/2610.19492/) (2026)

## Abstract (arXiv)

Inverse text normalization (ITN) converts spoken-domain automatic speech recognition (ASR) output into written-domain text to improve the readability of the ASR output. Many state-of-the-art ITN systems use hand-written weighted finite-state transducer(WFST) grammars since this task has extremely low tolerance to unrecoverable errors. We introduce an open-source Python WFST-based library for ITN which enables a seamless path from development to production. We describe the specification of ITN grammar rules for English, but the library can be adapted for other languages. It can also be used for written-to-spoken text normalization. We evaluate the NeMo ITN library using a modified version of the Google Text normalization dataset.