Abstract
Automatic Speech Recognition (ASR) in dialect-heavy settings remains challenging due to strong regional variation and limited labeled data. We propose GLoRIA, a parameter-efficient adaptation framework that leverages metadata (e.g., coordinates) to modulate low-rank updates in a pre-trained encoder. GLoRIA injects low-rank matrices into each feed-forward layer, with a gating MLP determining the non-negative contribution of each LoRA rank-1 component based on location metadata. On the GCND corpus, GLoRIA outperforms geo-conditioned full fine-tuning, LoRA, and both dialect-specific and unified full fine-tuning, achieving state-of-the-art word error rates while updating under 10% of parameters. GLoRIA also generalizes well to unseen dialects, including in extrapolation scenarios, and enables interpretable adaptation patterns that can be visualized geospatially. These results show metadata-gated low-rank adaptation is an effective, interpretable, and efficient solution for dialectal ASR.
Abstract (translated)
在方言浓厚的环境下，自动语音识别（ASR）依然面临挑战，这主要是由于区域差异显著以及标注数据有限。为此，我们提出了一种名为GLoRIA的参数高效适应框架，该框架利用元数据（如坐标信息）来调节预训练编码器中的低秩更新。GLoRIA在每个前馈层中注入了低秩矩阵，并使用一个门控多层感知机（MLP）根据位置元数据确定每种LoRA秩-1成分的非负贡献度。 在GCND语料库上的实验表明，GLoRIA的表现优于地理条件下的全量微调、LoRA方法以及特定方言和统一全量微调方案，同时它仅更新不到10%的参数就能实现最优的话音错误率。此外，该框架还能很好地泛化到未见过的方言中，包括在推断场景下也能保持性能，并且能够生成可解释的适应模式，这些模式可以以地理空间的方式进行可视化。 实验结果表明，基于元数据门控的低秩适应是一种有效、可解释和高效的解决方案，适用于解决方言ASR问题。
URL
https://arxiv.org/abs/2603.02464
