大规模、公开授权的语音数据集对于构建自动语音识别（ASR）系统至关重要，然而许多广泛使用的语言在公共资源中仍然代表性不足。帕什托语，由超过6000万人使用，历史上缺乏适合现代ASR开发的大规模公开授权语音数据。本文对Mozilla Common Voice语料库中的帕什托语部分进行了释放级分析，重点关注版本24.0（2025年12月）以及主要版本中的趋势。我们记录了从2023年中期的1.49小时录制时间增长到2025年的2768.7小时总时间，其中包括975.89小时可用于监督ASR训练的验证时间。除了规模之外，我们还分析了验证吞吐量、贡献者参与不平等、人口统计数据的完整性以及验证子集中的句子级集中情况。我们发现参与度极不均衡（基尼系数=0.941），年龄代表性强烈偏向年轻人，且41.97%的片段缺乏自我报告的性别标签，限制了基于元数据的子组审计。在文本层面，提示的重复使用程度适中：35.88%的唯一句子占了50%的验证片段，表明结构性集中主要由不均等的贡献者活动驱动，而非少数提示集的主导。这些结果提供了对快速扩展的低资源语音语料库的定量审计，并突显了改进数据集成熟度的实用优先事项，包括扩展验证能力以及更广泛的人口参与度。
英文摘要：
Large, openly licensed speech datasets are essential for building automatic speech recognition (ASR) systems, yet many widely spoken languages remain underrepresented in public resources. Pashto, spoken by more than 60 million people, has historically lacked large-scale openly licensed speech data suitable for modern ASR development.
This paper presents a release-level analysis of the Pashto component of the Mozilla Common Voice corpus, focusing on version 24.0 (December 2025) and contextualizing trends across major releases. We document rapid growth from 1.49 recorded hours in mid-2023 to 2,768.7 total hours in 2025, including 975.89 validated hours available for supervised ASR training.
Beyond scale, we analyze validation throughput, contributor participation inequality, demographic metadata completeness, and sentence-level concentration in the validated subset. We find that participation is extremely concentrated (Gini = 0.941), age representation is strongly skewed toward young adults, and 41.97\% of clips lack self-reported gender labels, limiting subgroup auditing based on metadata. At the textual level, prompt reuse is moderate: 35.88\% of unique sentences account for 50\% of validated clips, suggesting that structural concentration is driven primarily by uneven contributor activity rather than dominance of a small prompt set.
These results provide a quantitative audit of a rapidly scaling low-resource speech corpus and highlight practical priorities for improving dataset maturity, including expanded validation capacity and broader demographic participation.
Overcoming Data Scarcity in Multi-Dialectal Arabic ASR via Whisper Fine-Tuning
Department of Internal Medicine, Division of Nephrology（内科学部，肾病科）；Bernoulli Institute for Mathematics, Computer Science（伯努利数学、计算机科学研究所）；Speech Technology Lab, Campus Fryslân（语音技术实验室，弗里西恩校区）
Vevo2: A Unified and Controllable Framework for Speech and Singing Voice Generation
The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；ByteDance Seed（字节跳动种子）；Shenzhen Loop Area Institute（深圳河套学院）；City University of Macau（澳门城市大学）；Amphion Technology Co., Ltd.（Amphion科技有限公司）
Efficient ASR Training with Conversations that Never Happened
Dept. of Telecommunications and Artificial Intelligence, Budapest University of Technology and Economics（电信与人工智能系，布达佩斯技术与经济大学）；SpeechTex Ltd.（SpeechTex公司）；ELTE Research Centre for Linguistics（ELTE语言研究所）
