MoLGE: Mixture of Language Group Experts for Efficient Scaling of Massively Multilingual Speech Recognition

2026-07-27 · Source: Computation and Language · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Natural Language Processing · Depth: Expert, quick

Summary

MoLGE, or Mixture of Language Group Experts, is a novel architecture designed to enhance massively multilingual automatic speech recognition (ASR) models, which often struggle with diluted capacity across hundreds of languages. Built upon speech self-supervised models (S3Ms), MoLGE assigns dedicated expert modules to clusters of similar languages, reducing the submodules needed compared to traditional language-specific Mixture-of-Experts (MoE) schemes. It integrates a hierarchical Low-Rank Adaptation (LoRA) strategy into S3M's acoustic and linguistic components for efficient language-specific modeling and parameter efficiency. The research also explores language grouping strategies, based on linguistic and data-driven criteria, to understand their impact on scalability. Evaluated on a benchmark of 495 languages, MoLGE consistently outperforms dense multilingual baselines with only a minimal increase in trainable parameters, showing substantial improvements in both phonetic and orthographic ASR modeling.

Key takeaway

For Machine Learning Engineers developing massively multilingual ASR systems, MoLGE offers a clear path to overcome the "curse of multilinguality." You should consider implementing language grouping strategies and hierarchical Low-Rank Adaptation (LoRA) within your S3M architectures. This approach allows your models to scale to hundreds of languages efficiently, improving performance across phonetic and orthographic aspects without a significant increase in trainable parameters.

Key insights

MoLGE uses language grouping and LoRA to efficiently scale multilingual ASR, overcoming capacity dilution.

Principles

• Grouping similar languages optimizes expert allocation.
• Hierarchical LoRA enables parameter-efficient specialization.
• Language structure impacts multilingual ASR scalability.

Method

MoLGE assigns expert modules to language clusters, integrating hierarchical LoRA into S3M's acoustic and linguistic components. It investigates linguistic and data-driven grouping strategies.

In practice

• Cluster languages by similarity for ASR expert assignment.
• Apply hierarchical LoRA for efficient language adaptation.
• Analyze language structure to improve multilingual system design.

Topics

• Multilingual ASR
• Mixture of Experts
• Low-Rank Adaptation
• Speech Self-Supervised Models (S3M)
• Language Grouping
• Parameter Efficiency

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computation and Language.