# Improving Data Driven Inverse Text Normalization using Data Augmentation

arXiv: https://arxiv.org/abs/2207.09674 · HTML: https://mlsanity.com/paper/2207.09674/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 15 references that are in the map.
- First posted on arXiv 20 Jul 2022.

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

- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [An Exploration of Word Embedding Initialization in Deep-Learning Tasks](https://mlsanity.com/paper/1711.09160/) (2017)
- [Text normalization using memory augmented neural networks](https://mlsanity.com/paper/1806.00044/) (2018)

## Built on by (most cited first, 3 of 3)

- [On the Effects of Heterogeneous Data Sources on Speech-to-Text Foundation Models](https://mlsanity.com/paper/2406.09282/) (2024)
- [Dynamic Context-Aware Streaming Pretrained Language Model For Inverse Text Normalization](https://mlsanity.com/paper/2505.24229/) (2025)
- [Improving Robustness of Neural Inverse Text Normalization via Data-Augmentation, Semi-Supervised Learning, and Post-Aligning Method](https://mlsanity.com/paper/2309.08626/) (2023)

## Abstract (arXiv)

Inverse text normalization (ITN) is used to convert the spoken form output of an automatic speech recognition (ASR) system to a written form. Traditional handcrafted ITN rules can be complex to transcribe and maintain. Meanwhile neural modeling approaches require quality large-scale spoken-written pair examples in the same or similar domain as the ASR system (in-domain data), to train. Both these approaches require costly and complex annotations. In this paper, we present a data augmentation technique that effectively generates rich spoken-written numeric pairs from out-of-domain textual data with minimal human annotation. We empirically demonstrate that ITN model trained using our data augmentation technique consistently outperform ITN model trained using only in-domain data across all numeric surfaces like cardinal, currency, and fraction, by an overall accuracy of 14.44%.