SLM Test-Time Adaptation for Robust Speech Applications

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.

• •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.