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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/2408.11873 |
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| _version_ | 1866911999051431936 |
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| author | Kan, Xuan Xiao, Yonghui Yang, Tien-Ju Chen, Nanxin Mathews, Rajiv |
| author_facet | Kan, Xuan Xiao, Yonghui Yang, Tien-Ju Chen, Nanxin Mathews, Rajiv |
| contents | This work explores the challenge of enhancing Automatic Speech Recognition (ASR) model performance across various user-specific domains while preserving user data privacy. We employ federated learning and parameter-efficient domain adaptation methods to solve the (1) massive data requirement of ASR models from user-specific scenarios and (2) the substantial communication cost between servers and clients during federated learning. We demonstrate that when equipped with proper adapters, ASR models under federated tuning can achieve similar performance compared with centralized tuning ones, thus providing a potential direction for future privacy-preserved ASR services. Besides, we investigate the efficiency of different adapters and adapter incorporation strategies under the federated learning setting. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_11873 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Parameter-Efficient Transfer Learning under Federated Learning for Automatic Speech Recognition Kan, Xuan Xiao, Yonghui Yang, Tien-Ju Chen, Nanxin Mathews, Rajiv Audio and Speech Processing Cryptography and Security Machine Learning This work explores the challenge of enhancing Automatic Speech Recognition (ASR) model performance across various user-specific domains while preserving user data privacy. We employ federated learning and parameter-efficient domain adaptation methods to solve the (1) massive data requirement of ASR models from user-specific scenarios and (2) the substantial communication cost between servers and clients during federated learning. We demonstrate that when equipped with proper adapters, ASR models under federated tuning can achieve similar performance compared with centralized tuning ones, thus providing a potential direction for future privacy-preserved ASR services. Besides, we investigate the efficiency of different adapters and adapter incorporation strategies under the federated learning setting. |
| title | Parameter-Efficient Transfer Learning under Federated Learning for Automatic Speech Recognition |
| topic | Audio and Speech Processing Cryptography and Security Machine Learning |
| url | https://arxiv.org/abs/2408.11873 |