English(EN) Mixture of Phonetic Experts Based Low-Rank Adaptation of Conformer Models for Accented English Speech Recognition

新的MoPE-LoRA框架改进了带口音英语的语音识别

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-21 04:00

研究人员开发了一种新的参数高效适配框架，称为基于语音专家混合的低秩适配器（MoPE-LoRA），用于带口音的英语语音识别。与之前为每种口音分配专家的方法不同，MoPE-LoRA围绕语音类别，特别是发音方式类别来组织适配。这种方法允许模型在不需要特定口音模块的情况下扩展到未见的口音。在L2-ARCTIC数据集上的实验表明，MoPE-LoRA的性能显著优于单一LoRA和完全微调，在零样本评估中相对提高了12.3%。 AI

影响为语音识别系统中的口音适配提供了一种更具可扩展性和可迁移性的方法。

排序理由详细介绍语音识别新方法的学术论文。[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) · Santosh Dahal, Anmol Guragain, Tianchi Liu, Luis Fernando D'Haro, Kiran Chandra Dahal · 2026-10-21 04:00

基于混合音素专家低秩适应的 Conformer 模型用于带口音英语语音识别

arXiv:2610.26888v1 Announce Type: new Abstract: We present Mixture of Phonetic Experts based Low-Rank Adaptation (MoPE-LoRA), a parameter-efficient adaptation framework for accented English speech recognition. Recent mixture-of-experts approaches typically allocate experts per ac…

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基于混合音素专家低秩适应的 Conformer 模型用于带口音英语语音识别

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