arXiv cs.CL· Katsumi Ibaraki, David Chiang·· 6 天前AI 评分22
错误率还不够：低资源 ASR 中数据增强的代价
Error Rate is Not Enough: Costs of Augmentation in Low-Resource ASR
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研究在四种低资源语言和英语、五种数据规模上交叉对比了音频类增强（speed perturbation、SpecAugment）与文本类增强，并在音素层面分析错误。
正文 · 原文
Abstract:Data augmentation is standard practice for low-resource automatic speech recognition (ASR), but its benefits are almost always reported as aggregate error rates, leaving open why and when a method helps. On four low-resource languages and English at five data sizes, we cross two augmentations that modify existing audio (speed perturbation and SpecAugment) with two that modify text transcriptions and synthesize audio from it. We analyze errors at the phoneme level and find that they help for complementary reasons. SpecAugment has the most reliable aggregate improvements, but increases tone and palatalization errors, whereas text-based methods introduce consonant insertions; in combination, each reduces the errors the other introduces. This complementarity is predictable. Tone and palatalization are each signaled by a single acoustic property and occur least frequently in training (lowest training exposure), and text-based methods that flatten word frequency raise their frequency by 37--38%, reversing those errors while further improving aggregate error. The improvement from combining the methods exceeds the sum of their individual improvements; the superadditivity vanishes by 100 hours, limiting the complementarity to the low-resource regime. Where specific error types matter, we recommend selecting augmentation by training exposure and acoustic properties, both measurable before any training.
| Comments: | 5 pages |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2610.13807 [cs.CL] |
| (or arXiv:2610.13807v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.13807 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Katsumi Ibaraki [view email]
[v1]
Fri, 9 Oct 2026 16:19:43 UTC (43 KB)
来源：arXiv cs.CL · arxiv.org