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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2411.18217 |
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| _version_ | 1866913635905830912 |
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| author | Wang, Shih-Heng Chen, Zih-Ching Shi, Jiatong Chuang, Ming-To Lin, Guan-Ting Huang, Kuan-Po Harwath, David Li, Shang-Wen Lee, Hung-yi |
| author_facet | Wang, Shih-Heng Chen, Zih-Ching Shi, Jiatong Chuang, Ming-To Lin, Guan-Ting Huang, Kuan-Po Harwath, David Li, Shang-Wen Lee, Hung-yi |
| contents | The utilization of speech Self-Supervised Learning (SSL) models achieves impressive performance on Automatic Speech Recognition (ASR). However, in low-resource language ASR, they encounter the domain mismatch problem between pre-trained and low-resource languages. Typical solutions like fine-tuning the SSL model suffer from high computation costs while using frozen SSL models as feature extractors comes with poor performance. To handle these issues, we extend a conventional efficient fine-tuning scheme based on the adapter. We add an extra intermediate adaptation to warm up the adapter and downstream model initialization. Remarkably, we update only 1-5% of the total model parameters to achieve the adaptation. Experimental results on the ML-SUPERB dataset show that our solution outperforms conventional efficient fine-tuning. It achieves up to a 28% relative improvement in the Character/Phoneme error rate when adapting to unseen languages. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_18217 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | How to Learn a New Language? An Efficient Solution for Self-Supervised Learning Models Unseen Languages Adaption in Low-Resource Scenario Wang, Shih-Heng Chen, Zih-Ching Shi, Jiatong Chuang, Ming-To Lin, Guan-Ting Huang, Kuan-Po Harwath, David Li, Shang-Wen Lee, Hung-yi Sound Computation and Language Audio and Speech Processing The utilization of speech Self-Supervised Learning (SSL) models achieves impressive performance on Automatic Speech Recognition (ASR). However, in low-resource language ASR, they encounter the domain mismatch problem between pre-trained and low-resource languages. Typical solutions like fine-tuning the SSL model suffer from high computation costs while using frozen SSL models as feature extractors comes with poor performance. To handle these issues, we extend a conventional efficient fine-tuning scheme based on the adapter. We add an extra intermediate adaptation to warm up the adapter and downstream model initialization. Remarkably, we update only 1-5% of the total model parameters to achieve the adaptation. Experimental results on the ML-SUPERB dataset show that our solution outperforms conventional efficient fine-tuning. It achieves up to a 28% relative improvement in the Character/Phoneme error rate when adapting to unseen languages. |
| title | How to Learn a New Language? An Efficient Solution for Self-Supervised Learning Models Unseen Languages Adaption in Low-Resource Scenario |
| topic | Sound Computation and Language Audio and Speech Processing |
| url | https://arxiv.org/abs/2411.18217 |