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arxiv: 2606.25436 · v1 · pith:PSNU4RKGnew · submitted 2026-06-24 · 📡 eess.AS · cs.CL· cs.SD

Evaluating Japanese Dialect Robustness Across Speech and Text-based Large Language Models

Tomoya Mizumoto , Yusuke Fujita , Hao Shi , Lianbo Liu , Atsushi Kojima , Yui Sudo This is my paper

Pith reviewed 2026-06-25 20:06 UTC · model grok-4.3

classification 📡 eess.AS cs.CLcs.SD
keywords Japanese dialectsspeech language modelslarge language modelsdialect robustnessrobustness evaluationspoken dialogue systemsSLMLLM
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The pith

Speech language models' dialect robustness correlates with their text LLM counterparts and improves with dialect training and encoder fine-tuning.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper investigates how well text-based large language models and speech language models handle Japanese dialects compared to standard Japanese. It defines robustness as the ratio of performance on dialectal versus standard inputs to allow fair comparisons across models. Experiments reveal that the robustness of SLMs correlates with the robustness of the text-based LLMs they build upon. Training SLMs with dialectal data and fine-tuning the speech encoder each lead to better robustness on dialect inputs. A reader would care because dialect variation poses a practical barrier to spoken dialogue systems that current models have not fully solved.

Core claim

Our experiments show that SLM robustness correlates with that of their text-based counterparts. Furthermore, training with dialectal data and fine-tuning the speech encoder each improves robustness in SLMs, where robustness is defined as the ratio of performance on dialectal versus standard inputs using Japanese dialects as the test case.

What carries the argument

The ratio of performance on dialectal versus standard inputs, used as the definition of robustness for comparing LLMs and SLMs on Japanese dialect tasks.

If this is right

  • SLM robustness can be inferred from the base text LLM's robustness without separate speech testing.
  • Adding dialectal data during training raises SLM performance on dialect inputs relative to standard ones.
  • Fine-tuning only the speech encoder component yields measurable robustness gains for spoken dialect inputs.
  • The performance-ratio metric enables direct comparison of dialect handling across text and speech models.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Selecting a base LLM already strong on text dialects would likely produce a more robust SLM without additional speech-specific work.
  • The observed correlation implies that most dialect difficulties reside in the language model core rather than the speech front-end.
  • If the correlation pattern appears in other languages, the same training and fine-tuning steps could transfer.
  • System builders could screen candidate base models on text dialect benchmarks before building the speech version.

Load-bearing premise

The ratio of performance on dialectal versus standard inputs constitutes a fair and sufficient definition of robustness, and the selected Japanese dialects, tasks, and models are representative enough for the correlation and improvement claims to generalize.

What would settle it

Finding an SLM whose dialect robustness does not track its text LLM counterpart, or showing that dialectal training and speech-encoder fine-tuning produce no gain on held-out dialect test sets.

Figures

Figures reproduced from arXiv: 2606.25436 by Atsushi Kojima, Hao Shi, Lianbo Liu, Tomoya Mizumoto, Yui Sudo, Yusuke Fujita.

Figure 1
Figure 1. Figure 1: An example of robustness evaluation using a [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Geographic distribution of the 20 dialects in the [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Robustness comparison between text and audio models. The x-axis and y-axis represent robustness scores [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Robustness comparison between audio models [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Robustness comparison between audio models [PITH_FULL_IMAGE:figures/full_fig_p006_5.png] view at source ↗
read the original abstract

Dialogue systems based on large language models (LLMs) have advanced significantly in recent years. However, dialectal variation remains a major challenge, particularly for systems that process spoken input. LLM-based speech language models (SLMs), which integrate LLMs with speech processing components, show promise for spoken language tasks, yet their ability to comprehend dialects has not been sufficiently studied. Moreover, it remains unclear how the dialectal understanding of the base LLM affects SLM performance. This study investigates the dialectal robustness of both LLMs and SLMs using Japanese dialects as a test case. We define robustness as the ratio of performance on dialectal versus standard inputs, enabling fair comparisons. Our experiments show that SLM robustness correlates with that of their text-based counterparts. Furthermore, training with dialectal data and fine-tuning the speech encoder each improves robustness in SLMs.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 1 minor

Summary. The paper evaluates dialectal robustness in Japanese LLMs and SLMs, defining robustness as the ratio of model performance on dialectal versus standard (standard Japanese) inputs. Experiments demonstrate a correlation between the robustness of SLMs and their text-based LLM counterparts. Additional results indicate that training on dialectal data and fine-tuning the speech encoder each improve robustness metrics in SLMs.

Significance. If the empirical patterns hold under a more robust metric and with representative sampling, the work would provide useful evidence on modality transfer of dialectal understanding and practical levers (dialectal training, encoder fine-tuning) for improving spoken dialogue systems. The ratio-based framing allows cross-model comparison but requires validation that it isolates generalization rather than baseline effects.

major comments (1)
  1. [Abstract and Results] The central claims (correlation between SLM and LLM robustness; gains from dialectal training and encoder fine-tuning) rest on defining robustness exclusively as the performance ratio (dialectal/standard). This ratio is only interpretable for generalization if standard-input performance is stable, high, and non-interacting with dialect effects across models. The manuscript should report absolute accuracies on standard inputs (with error bars) and test whether the ratio masks absolute performance drops or baseline variation; without these, the correlation and improvement claims risk being metric artifacts. (See abstract and results sections describing the ratio definition and reported correlations.)
minor comments (1)
  1. [Abstract] The abstract states the central findings but omits dataset sizes, number of dialects/tasks, model families, statistical tests, and controls; these details should be summarized early for readers.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their thoughtful review and constructive suggestion regarding the robustness metric. We address the major comment point-by-point below.

read point-by-point responses

  1. Referee: [Abstract and Results] The central claims (correlation between SLM and LLM robustness; gains from dialectal training and encoder fine-tuning) rest on defining robustness exclusively as the performance ratio (dialectal/standard). This ratio is only interpretable for generalization if standard-input performance is stable, high, and non-interacting with dialect effects across models. The manuscript should report absolute accuracies on standard inputs (with error bars) and test whether the ratio masks absolute performance drops or baseline variation; without these, the correlation and improvement claims risk being metric artifacts. (See abstract and results sections describing the ratio definition and reported correlations.)

    Authors: We agree that absolute accuracies on standard inputs provide valuable context. In the revised manuscript we will add a table (or supplementary figure) reporting absolute performance on standard Japanese inputs for every model, with error bars from repeated runs where available. We will also include a brief analysis checking for interactions between baseline performance and dialect effects. The ratio definition was selected to normalize for inherent differences in model capability on standard inputs and thereby enable fair cross-model comparisons of relative robustness; the observed correlation between SLM and LLM robustness, as well as the gains from dialectal training and encoder fine-tuning, remain consistent under this normalization. We will make these additions without altering the core claims. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical ratios and measured effects are reported directly from data

full rationale

The paper is an empirical evaluation study. It explicitly defines robustness as the performance ratio (dialectal/standard) and reports measured correlations plus training effects without any derivation chain, equations, or self-citations that reduce the central claims to fitted parameters or prior results by construction. The claims rest on experimental outcomes rather than self-referential logic, making the work self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claims rest on the assumption that performance ratios on chosen dialect/standard pairs are a valid robustness metric and that the tested models and tasks are representative; no free parameters or invented entities are introduced.

axioms (1)
  • domain assumption Performance ratio between dialectal and standard inputs is a sufficient and fair measure of dialectal robustness.
    The definition of robustness and all subsequent claims depend on this metric.

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discussion (0)

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