SE/BN Adapter: Parametric Efficient Domain Adaptation for Speaker Recognition
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866929382673612800 |
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| author | Wang, Tianhao Li, Lantian Wang, Dong |
| author_facet | Wang, Tianhao Li, Lantian Wang, Dong |
| contents | Deploying a well-optimized pre-trained speaker recognition model in a new domain often leads to a significant decline in performance. While fine-tuning is a commonly employed solution, it demands ample adaptation data and suffers from parameter inefficiency, rendering it impractical for real-world applications with limited data available for model adaptation. Drawing inspiration from the success of adapters in self-supervised pre-trained models, this paper introduces a SE/BN adapter to address this challenge. By freezing the core speaker encoder and adjusting the feature maps' weights and activation distributions, we introduce a novel adapter utilizing trainable squeeze-and-excitation (SE) blocks and batch normalization (BN) layers, termed SE/BN adapter. Our experiments, conducted using VoxCeleb for pre-training and 4 genres from CN-Celeb for adaptation, demonstrate that the SE/BN adapter offers significant performance improvement over the baseline and competes with the vanilla fine-tuning approach by tuning just 1% of the parameters. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_07832 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | SE/BN Adapter: Parametric Efficient Domain Adaptation for Speaker Recognition Wang, Tianhao Li, Lantian Wang, Dong Sound Audio and Speech Processing Deploying a well-optimized pre-trained speaker recognition model in a new domain often leads to a significant decline in performance. While fine-tuning is a commonly employed solution, it demands ample adaptation data and suffers from parameter inefficiency, rendering it impractical for real-world applications with limited data available for model adaptation. Drawing inspiration from the success of adapters in self-supervised pre-trained models, this paper introduces a SE/BN adapter to address this challenge. By freezing the core speaker encoder and adjusting the feature maps' weights and activation distributions, we introduce a novel adapter utilizing trainable squeeze-and-excitation (SE) blocks and batch normalization (BN) layers, termed SE/BN adapter. Our experiments, conducted using VoxCeleb for pre-training and 4 genres from CN-Celeb for adaptation, demonstrate that the SE/BN adapter offers significant performance improvement over the baseline and competes with the vanilla fine-tuning approach by tuning just 1% of the parameters. |
| title | SE/BN Adapter: Parametric Efficient Domain Adaptation for Speaker Recognition |
| topic | Sound Audio and Speech Processing |
| url | https://arxiv.org/abs/2406.07832 |