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

arXiv: https://arxiv.org/abs/2505.20006 · HTML: https://mlsanity.com/paper/2505.20006/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 26 references that are in the map.
- First posted on arXiv 26 May 2025.

## Builds on (most cited first, 12 of 12)

- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Parameter-Efficient Transfer Learning for NLP](https://mlsanity.com/paper/1902.00751/) (2019)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [DoRA: Weight-Decomposed Low-Rank Adaptation](https://mlsanity.com/paper/2402.09353/) (2024)
- [LoRA+: Efficient Low Rank Adaptation of Large Models](https://mlsanity.com/paper/2402.12354/) (2024)
- [MixLoRA: Enhancing Large Language Models Fine-Tuning with LoRA-based Mixture of Experts](https://mlsanity.com/paper/2404.15159/) (2024)
- [Higher Layers Need More LoRA Experts](https://mlsanity.com/paper/2402.08562/) (2024)
- [MoELoRA: Contrastive Learning Guided Mixture of Experts on Parameter-Efficient Fine-Tuning for Large Language Models](https://mlsanity.com/paper/2402.12851/) (2024)
- [Multi-Dialect Speech Recognition With A Single Sequence-To-Sequence Model](https://mlsanity.com/paper/1712.01541/) (2017)
- [LoRA-Whisper: Parameter-Efficient and Extensible Multilingual ASR](https://mlsanity.com/paper/2406.06619/) (2024)
- [Layer-wise Fast Adaptation for End-to-End Multi-Accent Speech Recognition](https://mlsanity.com/paper/2204.09883/) (2022)
- [Non-native children speech recognition through transfer learning](https://mlsanity.com/paper/1809.09658/) (2018)

## Built on by (most cited first, 5 of 5)

- [Accent-Invariant Automatic Speech Recognition via Saliency-Driven Spectrogram Masking](https://mlsanity.com/paper/2510.09528/) (2025)
- [A Language-Agnostic Hierarchical LoRA-MoE Architecture for CTC-based Multilingual ASR](https://mlsanity.com/paper/2601.00557/) (2026)
- [Contrastive Regularization for Accent-Robust ASR](https://mlsanity.com/paper/2605.03297/) (2026)
- [MoLGE: Mixture of Language Group Experts for Efficient Scaling of Massively Multilingual Speech Recognition](https://mlsanity.com/paper/2607.24030/) (2026)
- [Beyond WER: Entity and Disfluency Recall in Accented Conversational ASR](https://mlsanity.com/paper/2609.20828/) (2026)

## Abstract (arXiv)

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.