# A hybrid text normalization system using multi-head self-attention for mandarin

arXiv: https://arxiv.org/abs/1911.04128 · HTML: https://mlsanity.com/paper/1911.04128/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Nov 2019.

## Built on by (most cited first, 5 of 5)

- [A Survey on Neural Speech Synthesis](https://mlsanity.com/paper/2106.15561/) (2021)
- [Review of end-to-end speech synthesis technology based on deep learning](https://mlsanity.com/paper/2104.09995/) (2021)
- [A unified front-end framework for English text-to-speech synthesis](https://mlsanity.com/paper/2305.10666/) (2023)
- [An End-to-end Chinese Text Normalization Model based on Rule-guided Flat-Lattice Transformer](https://mlsanity.com/paper/2203.16954/) (2022)
- [A Novel Chinese Dialect TTS Frontend with Non-Autoregressive Neural Machine Translation](https://mlsanity.com/paper/2206.04922/) (2022)

## Abstract (arXiv)

In this paper, we propose a hybrid text normalization system using multi-head self-attention. The system combines the advantages of a rule-based model and a neural model for text preprocessing tasks. Previous studies in Mandarin text normalization usually use a set of hand-written rules, which are hard to improve on general cases. The idea of our proposed system is motivated by the neural models from recent studies and has a better performance on our internal news corpus. This paper also includes different attempts to deal with imbalanced pattern distribution of the dataset. Overall, the performance of the system is improved by over 1.5% on sentence-level and it has a potential to improve further.