Research Paper#Speech Processing, Machine Learning, Test-Time Adaptation🔬 ResearchAnalyzed: Jan 3, 2026 08:44
SLM Test-Time Adaptation for Robust Speech Applications
Published:Dec 31, 2025 09:13
•1 min read
•ArXiv
Analysis
This paper addresses a critical problem in spoken language models (SLMs): their vulnerability to acoustic variations in real-world environments. The introduction of a test-time adaptation (TTA) framework is significant because it offers a more efficient and adaptable solution compared to traditional offline domain adaptation methods. The focus on generative SLMs and the use of interleaved audio-text prompts are also noteworthy. The paper's contribution lies in improving robustness and adaptability without sacrificing core task accuracy, making SLMs more practical for real-world applications.
Key Takeaways
- •Introduces a test-time adaptation (TTA) framework for generative Spoken Language Models (SLMs).
- •Adapts a small subset of parameters during inference using only the incoming utterance.
- •Improves robustness to acoustic variability without degrading core task accuracy.
- •Efficient in terms of compute and memory, suitable for resource-constrained platforms.
Reference
“Our method updates a small, targeted subset of parameters during inference using only the incoming utterance, requiring no source data or labels.”