# Proteno: Text Normalization with Limited Data for Fast Deployment in Text to Speech Systems

arXiv: https://arxiv.org/abs/2104.07777 · HTML: https://mlsanity.com/paper/2104.07777/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 21 references that are in the map.
- First posted on arXiv 15 Apr 2021.

## Builds on (most cited first, 7 of 7)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Bidirectional LSTM-CRF Models for Sequence Tagging](https://mlsanity.com/paper/1508.01991/) (2015)
- [Advances in Pre-Training Distributed Word Representations](https://mlsanity.com/paper/1712.09405/) (2017)
- [RNN Approaches to Text Normalization: A Challenge](https://mlsanity.com/paper/1611.00068/) (2016)
- [Scalable Multilingual Frontend for TTS](https://mlsanity.com/paper/2004.04934/) (2020)
- [Text normalization using memory augmented neural networks](https://mlsanity.com/paper/1806.00044/) (2018)

## Built on by (most cited first, 7 of 7)

- [A Chat About Boring Problems: Studying GPT-based text normalization](https://mlsanity.com/paper/2309.13426/) (2023)
- [A Unified Transformer-based Framework for Duplex Text Normalization](https://mlsanity.com/paper/2108.09889/) (2021)
- [Transformer-based Models of Text Normalization for Speech Applications](https://mlsanity.com/paper/2202.00153/) (2022)
- [Thutmose Tagger: Single-pass neural model for Inverse Text Normalization](https://mlsanity.com/paper/2208.00064/) (2022)
- [Non-Standard Vietnamese Word Detection and Normalization for Text-to-Speech](https://mlsanity.com/paper/2209.02971/) (2022)
- [Multi-Task Learning for Front-End Text Processing in TTS](https://mlsanity.com/paper/2401.06321/) (2024)
- [Positional Description for Numerical Normalization](https://mlsanity.com/paper/2408.12430/) (2024)

## Abstract (arXiv)

Developing Text Normalization (TN) systems for Text-to-Speech (TTS) on new languages is hard. We propose a novel architecture to facilitate it for multiple languages while using data less than 3% of the size of the data used by the state of the art results on English. We treat TN as a sequence classification problem and propose a granular tokenization mechanism that enables the system to learn majority of the classes and their normalizations from the training data itself. This is further combined with minimal precoded linguistic knowledge for other classes. We publish the first results on TN for TTS in Spanish and Tamil and also demonstrate that the performance of the approach is comparable with the previous work done on English. All annotated datasets used for experimentation will be released at https://github.com/amazon-research/proteno.