MoLGE: Mixture of Language Group Experts for Efficient Scaling of Massively Multilingual Speech Recognition
Massively multilingual automatic speech recognition (ASR) models covering hundreds of languages must maintain robust performance across diverse linguistic and acoustic conditions. However, these models often encounter the curse of multilinguality, where model capacity is diluted across languages. To address this challenge, we propose Mixture of Language Group Experts (MoLGE), built upon speech self-supervised models (S3Ms). MoLGE assigns dedicated expert modules to clusters of similar languages, reducing the number of required submodules compared to conventional language-specific Mixture-of-Experts (MoE) schemes. It further integrates a hierarchical Low-Rank Adaptation (LoRA) strategy int...
| Authors | Sangmin Lee, Woojin Chung, Woongjib Choi et al. |
|---|---|
| Primary category | cs.CL |
| Published | 27 July 2026 |
| Last updated at source | 27 July 2026 |
Connected in the knowledge graph
- Sanghyang00/omniasr-molgeDataset · cited by
Models and datasets referencing this paper
- Sanghyang00/omniasr-molgeDataset
Silytics shows public metadata from arXiv and links to the original. Information may be incomplete or out of date. Last refreshed 17 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.