English(EN) Encoder Awakening via Adapters: Effective Domain-Adaptive Fine-tuning of Speech-LLMs

新的EAVA方法增强了语音大语言模型在ASR领域的适应性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-17 04:00

研究人员开发了一种名为“通过适配器唤醒编码器”（EAVA）的新方法，以改进语音大语言模型（Speech-LLMs）在自动语音识别（ASR）领域的领域自适应微调。该方法通过在语音编码器的每一层训练轻量级适配器，在保留预训练信息的同时融入目标领域声学知识。随后，使用低秩适配器（LoRA）对整个模型在LLM上进行联合微调。实验表明，EAVA在包括儿童和方言语音在内的领域偏移数据集上超越了现有方法，确立了新的最先进性能。 AI

影响提高了在领域偏移语音上的ASR性能，可能为不同用户群体提供更鲁棒的语音接口。

排序理由详细介绍一种用于微调语音大语言模型新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.CL TIER_1 English(EN) · Mohan Shi, Zilai Wang, Natarajan Balaji Shankar, Kaiyuan Zhang, Eray Eren, Abeer Alwan · 2026-09-17 04:00

通过适配器实现编码器觉醒：语音大模型的有效领域自适应微调

arXiv:2609.17981v1 Announce Type: cross Abstract: Speech Large Language Models (Speech-LLMs), typically built from a pre-trained speech encoder, a modality projector, and an LLM fine-tuned with Low-Rank Adapters (LoRA), have shown strong Automatic Speech Recognition (ASR) perform…

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报道来源 [1]

通过适配器实现编码器觉醒：语音大模型的有效领域自适应微调

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