WildElder：一个具有细粒度人工标注的野生中文老年语音数据集
WildElder: A Chinese Elderly Speech Dataset from the Wild with Fine-Grained Manual Annotations
AI总结：
针对老年语音自动处理挑战及现有中文数据集局限，提出WildElder，从在线视频收集普通话老年语音语料库并细粒度标注，结合野外数据真实性与专家策划，可有力研究自动语音识别等，是有潜力的新基准。
摘要：
老年语音由于诸如发音缓慢和声音颤抖等与年龄相关的变化，给自动处理带来了独特挑战。现有的中文数据集大多在受控环境中录制，限制了其多样性和现实世界适用性。为填补这一空白，我们展示了WildElder，一个从在线视频收集的普通话老年语音语料库，并丰富了细粒度人工标注，包括转录、说话者年龄、性别和口音强度。将野外数据的真实性与专家策划相结合，WildElder能够对自动语音识别和说话者剖析进行有力研究。实验结果揭示了老年语音识别的困难以及WildElder作为一个具有挑战性的新基准的潜力。数据集和代码可通过此https网址获取。
英文摘要：
Elderly speech poses unique challenges for automatic processing due to age-related changes such as slower articulation and vocal tremors. Existing Chinese datasets are mostly recorded in controlled environments, limiting their diversity and real-world applicability. To address this gap, we present WildElder, a Mandarin elderly speech corpus collected from online videos and enriched with fine-grained manual annotations, including transcription, speaker age, gender, and accent strength. Combining the realism of in-the-wild data with expert curation, WildElder enables robust research on automatic speech recognition and speaker profiling. Experimental results reveal both the difficulties of elderly speech recognition and the potential of WildElder as a challenging new benchmark. The dataset and code are available at https://github.com/NKU-HLT/WildElder.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
