# Non-Standard Vietnamese Word Detection and Normalization for Text-to-Speech

arXiv: https://arxiv.org/abs/2209.02971 · HTML: https://mlsanity.com/paper/2209.02971/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 29 references that are in the map.
- First posted on arXiv 7 Sep 2022.

## Builds on (most cited first, 9 of 9)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [On the Properties of Neural Machine Translation: Encoder-Decoder Approaches](https://mlsanity.com/paper/1409.1259/) (2014)
- [End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF](https://mlsanity.com/paper/1603.01354/) (2016)
- [RNN Approaches to Text Normalization: A Challenge](https://mlsanity.com/paper/1611.00068/) (2016)
- [Improving Vietnamese Named Entity Recognition from Speech Using Word Capitalization and Punctuation Recovery Models](https://mlsanity.com/paper/2010.00198/) (2020)
- [Semi-supervised URL Segmentation with Recurrent Neural Networks Pre-trained on Knowledge Graph Entities](https://mlsanity.com/paper/2011.03138/) (2020)
- [Proteno: Text Normalization with Limited Data for Fast Deployment in Text to Speech Systems](https://mlsanity.com/paper/2104.07777/) (2021)
- [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)

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

- [A Weakly Supervised Data Labeling Framework for Machine Lexical Normalization in Vietnamese Social Media](https://mlsanity.com/paper/2409.20467/) (2024)
- [VietNormalizer: An Open-Source, Dependency-Free Python Library for Vietnamese Text Normalization in TTS and NLP Applications](https://mlsanity.com/paper/2603.04145/) (2026)
- [Complex-Text Robustness Evaluation and Failure Diagnosis for Low-Resource Multilingual Text-to-Speech](https://mlsanity.com/paper/2609.11545/) (2026)

## Abstract (arXiv)

Converting written texts into their spoken forms is an essential problem in any text-to-speech (TTS) systems. However, building an effective text normalization solution for a real-world TTS system face two main challenges: (1) the semantic ambiguity of non-standard words (NSWs), e.g., numbers, dates, ranges, scores, abbreviations, and (2) transforming NSWs into pronounceable syllables, such as URL, email address, hashtag, and contact name. In this paper, we propose a new two-phase normalization approach to deal with these challenges. First, a model-based tagger is designed to detect NSWs. Then, depending on NSW types, a rule-based normalizer expands those NSWs into their final verbal forms. We conducted three empirical experiments for NSW detection using Conditional Random Fields (CRFs), BiLSTM-CNN-CRF, and BERT-BiGRU-CRF models on a manually annotated dataset including 5819 sentences extracted from Vietnamese news articles. In the second phase, we propose a forward lexicon-based maximum matching algorithm to split down the hashtag, email, URL, and contact name. The experimental results of the tagging phase show that the average F1 scores of the BiLSTM-CNN-CRF and CRF models are above 90.00%, reaching the highest F1 of 95.00% with the BERT-BiGRU-CRF model. Overall, our approach has low sentence error rates, at 8.15% with CRF and 7.11% with BiLSTM-CNN-CRF taggers, and only 6.67% with BERT-BiGRU-CRF tagger.