Recent research has demonstrated that training a linear connector between speech foundation encoders and large language models (LLMs) enables this architecture to achieve strong ASR capabilities. Despite the impressive results, it remains unclear whether these simple approaches are robust enough across different scenarios and speech conditions, such as domain shifts and different speech perturbations. In this paper, we address these questions by conducting various ablation experiments using a recent and widely adopted approach called SLAM-ASR. We present novel empirical findings that offer insights on how to effectively utilize the SLAM-ASR architecture across a wide range of settings. Our main findings indicate that the SLAM-ASR exhibits poor performance in cross-domain evaluation settings. Additionally, speech perturbations within in-domain data, such as changes in speed or the presence of additive noise, can significantly impact performance. Our findings offer critical insights for fine-tuning and configuring robust LLM-based ASR models, tailored to different data characteristics and computational resources.

本研究针对SLAM-ASR在不同场景和语音条件下的鲁棒性进行了深入探讨，填补了当前简单方法可能不够稳健的研究空白。通过一系列消融实验，我们提出了新的实证发现，揭示了如何有效利用SLAM-ASR架构。研究结果表明，SLAM-ASR在跨领域评估中表现不佳，且语音的扰动会显著影响其性能，从而为基于LLM的ASR模型的优化提供了宝贵的见解。

SLAM-ASR的性能评估：优点、缺点、挑战及前进方向