# Improving Vietnamese Named Entity Recognition from Speech Using Word Capitalization and Punctuation Recovery Models

arXiv: https://arxiv.org/abs/2010.00198 · HTML: https://mlsanity.com/paper/2010.00198/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Oct 2020.

## Built on by (most cited first, 11 of 11)

- [BARTpho: Pre-trained Sequence-to-Sequence Models for Vietnamese](https://mlsanity.com/paper/2109.09701/) (2021)
- [End-to-end Speech-to-Punctuated-Text Recognition](https://mlsanity.com/paper/2207.03169/) (2022)
- [Non-Standard Vietnamese Word Detection and Normalization for Text-to-Speech](https://mlsanity.com/paper/2209.02971/) (2022)
- [AdaCS: Adaptive Normalization for Enhanced Code-Switching ASR](https://mlsanity.com/paper/2501.07102/) (2025)
- [Vietnamese Capitalization and Punctuation Recovery Models](https://mlsanity.com/paper/2207.01312/) (2022)
- [Efficient Ensemble for Multimodal Punctuation Restoration using Time-Delay Neural Network](https://mlsanity.com/paper/2302.13376/) (2023)
- [Enhancing Clinical Documentation with Synthetic Data: Leveraging Generative Models for Improved Accuracy](https://mlsanity.com/paper/2406.06569/) (2024)
- [Medical Spoken Named Entity Recognition](https://mlsanity.com/paper/2406.13337/) (2024)
- [ViWOZ: A Multi-Domain Task-Oriented Dialogue Systems Dataset For Low-resource Language](https://mlsanity.com/paper/2203.07742/) (2022)
- [Dialogue Systems Can Generate Appropriate Responses without the Use of Question Marks? -- Investigation of the Effects of Question Marks on Dialogue Systems](https://mlsanity.com/paper/2308.03293/) (2023)
- [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)

Studies on the Named Entity Recognition (NER) task have shown outstanding results that reach human parity on input texts with correct text formattings, such as with proper punctuation and capitalization. However, such conditions are not available in applications where the input is speech, because the text is generated from a speech recognition system (ASR), and that the system does not consider the text formatting. In this paper, we (1) presented the first Vietnamese speech dataset for NER task, and (2) the first pre-trained public large-scale monolingual language model for Vietnamese that achieved the new state-of-the-art for the Vietnamese NER task by 1.3% absolute F1 score comparing to the latest study. And finally, (3) we proposed a new pipeline for NER task from speech that overcomes the text formatting problem by introducing a text capitalization and punctuation recovery model (CaPu) into the pipeline. The model takes input text from an ASR system and performs two tasks at the same time, producing proper text formatting that helps to improve NER performance. Experimental results indicated that the CaPu model helps to improve by nearly 4% of F1-score.