# Thai Wav2Vec2.0 with CommonVoice V8

arXiv: https://arxiv.org/abs/2208.04799 · HTML: https://mlsanity.com/paper/2208.04799/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 12 references that are in the map.
- First posted on arXiv 9 Aug 2022.

## Builds on (most cited first, 2 of 2)

- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [Common Voice: A Massively-Multilingual Speech Corpus](https://mlsanity.com/paper/1912.06670/) (2019)

## Built on by (most cited first, 2 of 2)

- [Combining Language Models For Specialized Domains: A Colorful Approach](https://mlsanity.com/paper/2310.19708/) (2023)
- [Domain-Adaptive ASR for Telephony AI Agents: Fine-tuning Canary Flash Models for Enterprise Contact Center Applications](https://mlsanity.com/paper/2608.24916/) (2026)

## Abstract (arXiv)

Recently, Automatic Speech Recognition (ASR), a system that converts audio into text, has caught a lot of attention in the machine learning community. Thus, a lot of publicly available models were released in HuggingFace. However, most of these ASR models are available in English; only a minority of the models are available in Thai. Additionally, most of the Thai ASR models are closed-sourced, and the performance of existing open-sourced models lacks robustness. To address this problem, we train a new ASR model on a pre-trained XLSR-Wav2Vec model with the Thai CommonVoice corpus V8 and train a trigram language model to boost the performance of our ASR model. We hope that our models will be beneficial to individuals and the ASR community in Thailand.