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arXiv · 2505.20006

Mixture of LoRA Experts for Low-Resourced Multi-Accent Automatic Speech Recognition

Raphaël Bagat·Irina Illina·Emmanuel Vincent
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Abstract

We aim to improve the robustness of Automatic Speech Recognition (ASR) systems against non-native speech, particularly in low-resourced multi-accent settings. We introduce Mixture of Accent-Specific LoRAs (MAS-LoRA), a fine-tuning method that leverages a mixture of Low-Rank Adaptation (LoRA) experts, each specialized in a specific accent. This method can be used when the accent is known or unknown at inference time, without the need to fine-tune the model again. Our experiments, conducted using Whisper on the L2-ARCTIC corpus, demonstrate significant improvements in Word Error Rate compared to regular LoRA and full fine-tuning when the accent is unknown. When the accent is known, the results further improve. Furthermore, MAS-LoRA shows less catastrophic forgetting than the other fine-tuning methods. To the best of our knowledge, this is the first use of a mixture of LoRA experts for non-native multi-accent ASR.

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Raphaël Bagat, Irina Illina, Emmanuel Vincent. 2025-05-26. Mixture of LoRA Experts for Low-Resourced Multi-Accent Automatic Speech Recognition. https://doi.org/10.21437/interspeech.2025-775

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