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arXiv · 2606.10464

GC-LoRA: Gated Convolutional LoRA for Parameter-Efficient Acoustic Adaptation

Natarajan Balaji Shankar·Zilai Wang·Kaiyuan Zhang·Mohan Shi·Abeer Alwan
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Abstract

Transformer-based Speech Foundation Models excel in most Automatic Speech Recognition tasks but often suffer performance degradation when applied to domains with mismatched acoustic characteristics. While Parameter Efficient Fine-Tuning (PEFT) methods, such as Low-Rank Adaptation (LoRA), adjust global attention, they lack the local context modeling crucial for capturing domain-specific variations. We propose GC-LoRA, a novel adapter architecture that injects Conformer-style local convolutional processing into pretrained Transformer encoders. By integrating a lightweight adapter to encoder attention output projections, our method efficiently captures local acoustic dependencies without disrupting pretrained global representations. Experiments across diverse datasets (acoustically-degraded, bandlimited, dialectal, child) demonstrate the efficacy of our approach, achieving Word Error Rate (WER) reductions of up to 10.9% compared to baselines while adding minimal trainable parameters.

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BibTeXRIS

Natarajan Balaji Shankar, Zilai Wang, Kaiyuan Zhang, Mohan Shi, Abeer Alwan. 2026-06-09. GC-LoRA: Gated Convolutional LoRA for Parameter-Efficient Acoustic Adaptation. https://arxiv.org/abs/2606.10464

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