---
格式版本: 2
标题: "myMediWhisper: Construction of Burmese Medical Speech Corpus and Whisper Fine-Tuning for Clinical Dialogue ASR"
原文链接: "https://arxiv.org/abs/2608.11036"
发布日期: "2026-08-11"
发布时间校准状态: "found"
发布时间需复核: "否"
发布时间来源: "rule:local:strict_original_body"
发布时间证据: "**\\[v1\\]** Tue, 11 Aug 2026 15:12:42 UTC (7,478 KB)"
发布时间校准原因: "规则确认唯一严格发布时间，来源 local:strict_original_body"
发布时间校准置信度: "high"
发布时间候选数量: 18
发布时间严格候选数量: 6
发布时间原页读取状态: "source template page reused from URL open"
发布时间未找到原因: ""
发布时间校准时间: "2026-08-12T11:19:53+08:00"
发布时间仲裁状态: "skipped"
发布时间仲裁尝试次数: 0
发布时间仲裁耗时毫秒: 0
发现时间: "2026-08-12T11:16:29+08:00"
入库时间: "2026-08-12T03:19:53.688Z"
来源平台: "arXiv 学术论文搜索"
搜索渠道: "source_template"
搜索词: "https://arxiv.org/search/?query=performance&searchtype=all"
匹配关键词:
  - "performance"
相关厂家:
  []
相关专家:
  []
内容类型: "网页"
抓取工具: "Free Fetch + Defuddle"
清洗工具: "Defuddle Markdown + Defuddle/Readability 正文提取"
原始附件:
  []
AI优质: "否"
AI打分: 10
AI分档: "非优质"
AI质检状态: "不通过"
AI打分理由: "资料为缅甸语医疗语音识别论文，与超节点/AI Rack/机柜级AI基础设施完全无关，属于明显无关内容。"
AI质检模型: "ali-deepseek-v4-flash"
AI质检时间: "2026-08-12T11:20:14+08:00"
AI主题相关性: 0
AI来源权威性: 5
AI新颖性: 0
AI技术细节: 0
AI商业部署信号: 0
AI完整性: 5
采集批次: "2026年8月12日2点04分20秒"
采集批次ID: "20260812-020420-289"
去重键: "https://arxiv.org/abs/2608.11036"
---

## Computer Science > Computation and Language

## Title:myMediWhisper: Construction of Burmese Medical Speech Corpus and Whisper Fine-Tuning for Clinical Dialogue ASR

[View PDF](https://arxiv.org/pdf/2608.11036) [HTML (experimental)](https://arxiv.org/html/2608.11036v1)

> Abstract:Although Whisper models benefit from large-scale multilingual pre-training, their performance on Burmese medical speech remains limited. This work presents a Burmese medical speech recognition framework built on a high-quality 28-hour corpus recorded and validated by native speakers. We fine-tune Whisper models using full fine-tuning (FFT) and parameter-efficient fine-tuning (PEFT) with LoRA. To evaluate robustness, we apply waveform- and spectrogram-level data augmentation under controlled noise and simulated room acoustics. While augmentation reduces performance on clean speech, it significantly improves robustness in noisy and reverberant environments across FFT and PEFT settings. Our best-performing system, fully fine-tuned myMediWhisper-Medium without augmentation, achieves a state-of-the-art Word Error Rate (WER) of 23.44%, outperforming much larger general-domain fine-tuned models. Dataset and other resources can be found at the Huggingface repository: [this https URL](https://huggingface.co/datasets/LULab/mediTalk-mm-rdy).

| Subjects: | Computation and Language (cs.CL) |
| --- | --- |
| Cite as: | [arXiv:2608.11036](https://arxiv.org/abs/2608.11036) \[cs.CL\] |
|  | (or [arXiv:2608.11036v1](https://arxiv.org/abs/2608.11036v1) \[cs.CL\] for this version) |
|  | [https://doi.org/10.48550/arXiv.2608.11036](https://doi.org/10.48550/arXiv.2608.11036) |

## Submission history

From: Thura Aung \[[view email](https://arxiv.org/show-email/1c503eee/2608.11036)\]  
**\[v1\]** Tue, 11 Aug 2026 15:12:42 UTC (7,478 KB)

[Which authors of this paper are endorsers?](https://arxiv.org/auth/show-endorsers/2608.11036) | Disable MathJax ([What is MathJax?](https://info.arxiv.org/help/mathjax.html))
