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[2506.07078] E-BATS: Efficient Backpropagation-Free Test-Time Adaptation for Speech Foundation Models
Summary
The paper presents E-BATS, a novel framework for efficient backpropagation-free test-time adaptation (TTA) tailored for speech foundation models, addressing performance issues in real-world scenarios with acoustic variability.
Why It Matters
As speech technology becomes increasingly prevalent, ensuring robust performance in diverse acoustic environments is critical. E-BATS offers a solution that balances efficiency and effectiveness, making it relevant for developers and researchers focused on speech processing and machine learning.
Key Takeaways
- E-BATS is designed specifically for speech foundation models, addressing unique challenges in acoustic variability.
- The framework achieves significant accuracy improvements (4.1%-13.5%) over existing backpropagation-free methods.
- E-BATS reduces GPU memory usage by 2.0-6.4 times compared to traditional backpropagation-based approaches.
- Key components include lightweight prompt adaptation and a multi-scale loss mechanism for effective feature alignment.
- The research paves the way for more scalable and efficient speech processing systems in real-world applications.
Computer Science > Machine Learning arXiv:2506.07078 (cs) [Submitted on 8 Jun 2025 (v1), last revised 23 Feb 2026 (this version, v3)] Title:E-BATS: Efficient Backpropagation-Free Test-Time Adaptation for Speech Foundation Models Authors:Jiaheng Dong, Hong Jia, Soumyajit Chatterjee, Abhirup Ghosh, James Bailey, Ting Dang View a PDF of the paper titled E-BATS: Efficient Backpropagation-Free Test-Time Adaptation for Speech Foundation Models, by Jiaheng Dong and 5 other authors View PDF HTML (experimental) Abstract:Speech Foundation Models encounter significant performance degradation when deployed in real-world scenarios involving acoustic domain shifts, such as background noise and speaker accents. Test-time adaptation (TTA) has recently emerged as a viable strategy to address such domain shifts at inference time without requiring access to source data or labels. However, existing TTA approaches, particularly those relying on backpropagation, are memory-intensive, limiting their applicability in speech tasks and resource-constrained settings. Although backpropagation-free methods offer improved efficiency, existing ones exhibit poor accuracy. This is because they are predominantly developed for vision tasks, which fundamentally differ from speech task formulations, noise characteristics, and model architecture, posing unique transferability challenges. In this paper, we introduce E-BATS, the first Efficient BAckpropagation-free TTA framework designed explicitly for speech fo...
