GLoRIA: Gated Low-Rank Interpretable Adaptation for Dialectal ASR

• URL: http://arxiv.org/abs/2603.02464v1
• Date: Mon, 02 Mar 2026 23:10:09 GMT
• Title: GLoRIA: Gated Low-Rank Interpretable Adaptation for Dialectal ASR
• Authors: Pouya Mehralian, Melissa Farasyn, Anne Breitbarth, Anne-Sophie Ghyselen, Hugo Van hamme,
• Abstract summary: GLoRIA is an adaptation framework that modulates low-rank updates in a pre-trained encoder.<n>On the GCND corpus, GLoRIA outperforms geo-conditioned full fine-tuning.
• Score: 11.705969106735454
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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