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Thai Wav2Vec2.0 with CommonVoice V8

A new ASR model is trained on a pre-trained XLSR-Wav2Vec model with the Thai CommonVoice dataset to improve performance and address the lack of robust open-sourced Thai ASR models.

Year
2022
Venue
arXiv 2022
Authors
5
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Abstract onlyARXIV-DEFAULT

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arxiv.org/abs/2208.04799ARXIV-DEFAULT
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Abstract

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.

Authors

5