哈萨克语语音的Wav2vec2模型微调:基于有限语料库的研究

2023 IEEE International Conference on Smart Information Systems and Technologies (SIST) Pub Date : 2023-05-04 DOI:10.1109/SIST58284.2023.10223504

Kairatuly Bauyrzhan, Mansurova Madina, Ospan Assel

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引用次数: 0

摘要

在这项研究中，我们通过对XLSR-Wav2Vec2预训练模型对哈萨克语语音语料库进行微调，开发了一个自动识别哈萨克语语音的模型。我们的结果表明，在一个小的哈萨克语语料库上微调wav2vec2模型可以显著提高识别精度。然而，需要更大的数据集来进一步评估这种方法的有效性。这项研究的结果有助于不断努力提高语音识别技术的低资源语言，如哈萨克语。

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Fine-Tuning the Wav2vec2 Model for Kazakh Speech: A Study on a Limited Corpus

In this study, we developed a model for automatic recognition of Kazakh speech by fine-tuning the XLSR-Wav2Vec2 pre-trained model to a corpus of Kazakh speech. Our results show that fine-tuning the wav2vec2 model on a small corpus of Kazakh speech allows a significant increase in recognition accuracy. However, larger datasets are needed to further evaluate the effectiveness of this approach. The results of this study contribute to ongoing efforts to improve speech recognition technology for low-resource languages such as Kazakh.

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来源期刊

2023 IEEE International Conference on Smart Information Systems and Technologies (SIST)

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