VARAN: Variational Inference for Self-Supervised Speech Models Fine-Tuning on Downstream Tasks
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866911107445161984 |
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| author | Diatlova, Daria Balagansky, Nikita Varlamov, Alexander Spirin, Egor |
| author_facet | Diatlova, Daria Balagansky, Nikita Varlamov, Alexander Spirin, Egor |
| contents | Conventional methods for aggregating layers in fine-tuned self-supervised speech models, such as using the final layer or weighted sum, suffer from information bottlenecks and static feature weighting for all dataset examples. We propose VARAN, a framework that dynamically tailors layer aggregation to individual inputs. By employing layer-specialized probing heads and data-dependent weighting, VARAN adaptively prioritizes layer's features based on input. Evaluations on automatic speech recognition and speech emotion recognition tasks demonstrate VARAN's superior performance, particularly when using the LoRA fine-tuning technique. The framework resolves the trade-off between preserving layer-specific information and enabling flexible feature utilization, advancing efficient adaptation of self-supervised speech representations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_12061 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | VARAN: Variational Inference for Self-Supervised Speech Models Fine-Tuning on Downstream Tasks Diatlova, Daria Balagansky, Nikita Varlamov, Alexander Spirin, Egor Machine Learning Conventional methods for aggregating layers in fine-tuned self-supervised speech models, such as using the final layer or weighted sum, suffer from information bottlenecks and static feature weighting for all dataset examples. We propose VARAN, a framework that dynamically tailors layer aggregation to individual inputs. By employing layer-specialized probing heads and data-dependent weighting, VARAN adaptively prioritizes layer's features based on input. Evaluations on automatic speech recognition and speech emotion recognition tasks demonstrate VARAN's superior performance, particularly when using the LoRA fine-tuning technique. The framework resolves the trade-off between preserving layer-specific information and enabling flexible feature utilization, advancing efficient adaptation of self-supervised speech representations. |
| title | VARAN: Variational Inference for Self-Supervised Speech Models Fine-Tuning on Downstream Tasks |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2508.12061 |