Dialect-Robust Speech Language Models with Synthetic Pseudo-Dialect Augmentation
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Computer Science > Computation and Language
arXiv:2610.09321 (cs)
[Submitted on 7 Oct 2026]
Title:Dialect-Robust Speech Language Models with Synthetic Pseudo-Dialect Augmentation
View a PDF of the paper titled Dialect-Robust Speech Language Models with Synthetic Pseudo-Dialect Augmentation, by Shunsuke Mitsumori and 2 other authors
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Abstract:Speech Language Model (SLM) performance often degrades on dialects due to data scarcity. Conventional text-to-speech (TTS) augmentation struggles to cover diverse dialects as it requires a certain amount of real dialect speech. We propose synthesizing pseudo-dialect speech by converting LLM-generated dialect text via a standard-language TTS model, requiring zero real dialect speech. Additionally, we introduce intermediate standard-text prediction during training, acting as semantic normalization for downstream tasks. We evaluate dialect understanding via dialect-to-English speech translation across Japanese, German, and Chinese dialects. Compared to synthetic standard speech baselines, pseudo-dialect augmentation improves scores for Japanese (from 25.38 to 26.24) and German (from 31.57 to 32.47). Furthermore, the intermediate standard-text prediction effectively bridges the semantic gap, boosting performance to 28.26 for Japanese and from 11.67 to 16.37 for Chinese. These results suggest that our approach scales to various languages without requiring speech resources specific to each dialect.
| Comments: | 7 pages, 1 figure, 7 tables. Accepted to IEEE SLT 2026 |
| Subjects: | Computation and Language (cs.CL); Audio and Speech Processing (eess.AS) |
| Cite as: | arXiv:2610.09321 [cs.CL] |
| (or arXiv:2610.09321v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.09321
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled Dialect-Robust Speech Language Models with Synthetic Pseudo-Dialect Augmentation, by Shunsuke Mitsumori and 2 other authors
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