Japanese dialects challenge LLMs: New study reveals robustness gaps
Research shows speech LLMs struggle with dialects—training on dialect data helps significantly.
A team of researchers (Tomoya Mizumoto, Yusuke Fujita, Hao Shi, Lianbo Liu, Atsushi Kojima, Yui Sudo) tested the dialectal robustness of both text-based large language models (LLMs) and speech language models (SLMs) that combine LLMs with speech processing. Using Japanese dialects—a notoriously diverse set of variants—they defined robustness as the ratio of model performance on dialectal inputs versus standard Japanese. This metric allowed fair comparisons across models of different sizes and architectures.
Their experiments revealed two critical insights. First, SLM robustness closely mirrors that of their underlying text-based LLM, meaning weaknesses in the base model propagate to the speech version. Second, targeted training on dialectal data significantly improves robustness, and fine-tuning the speech encoder (the component that processes raw audio) provides an additional boost. The paper, accepted to ASRU2025, underscores that dialect handling remains a major hurdle for spoken dialogue systems—even as LLMs achieve near-human performance on standard language tasks.
- Robustness defined as performance ratio of dialectal to standard Japanese inputs, enabling cross-model comparison.
- SLM robustness correlates with its base LLM—dialect weaknesses in the text model carry over to the speech model.
- Training on dialect data plus fine-tuning the speech encoder yields the best dialect comprehension gains.
Why It Matters
As voice assistants go global, handling dialects is essential—this study shows a clear path to improvement.