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arXiv · 2512.22165

Marco-ASR: A Principled and Metric-Driven Framework for Fine-Tuning Large-Scale ASR Models for Domain Adaptation

Xuanfan Ni·Fei Yang·Fengping Tian·Qingjuan Li·Chenyang Lyu·Yichao Du·Longyue Wang·Weihua Luo·Kaifu Zhang
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Abstract

Automatic Speech Recognition (ASR) models have achieved remarkable accuracy in general settings, yet their performance often degrades in domain-specific applications due to data mismatch and linguistic variability. This challenge is amplified for modern Large Language Model (LLM)-based ASR systems, whose massive scale and complex training dynamics make effective fine-tuning non-trivial. To address this gap, this paper proposes a principled and metric-driven fine-tuning framework for adapting both traditional and LLM-based ASR models to specialized domains. The framework emphasizes learning rate optimization based on performance metrics, combined with domain-specific data transformation and augmentation. We empirically evaluate our framework on state-of-the-art models, including Whisper, Whisper-Turbo, and Qwen2-Audio, across multi-domain, multilingual, and multi-length datasets. Our results not only validate the proposed framework but also establish practical protocols for improving domain-specific ASR performance while preventing overfitting.

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Xuanfan Ni, Fei Yang, Fengping Tian, Qingjuan Li, Chenyang Lyu, Yichao Du, Longyue Wang, Weihua Luo, Kaifu Zhang. 2025-12-17. Marco-ASR: A Principled and Metric-Driven Framework for Fine-Tuning Large-Scale ASR Models for Domain Adaptation. https://arxiv.org/abs/2512.22165

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