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.

翻译：老年语音因其特有的年龄相关变化（如语速减慢和发声震颤）给自动处理带来独特挑战。现有中文数据集大多在受控环境下录制，限制了其多样性和实际应用价值。为填补这一空白，我们提出了WildElder——一个从在线视频收集的汉语老年语音语料库，并辅以细粒度人工标注，包括转写文本、说话人年龄、性别及口音强度。WildElder融合了真实场景数据的现实性与专家标注的精确性，为自动语音识别和说话人特征分析提供了稳健的研究基础。实验结果表明了老年语音识别的困难性，同时揭示了WildElder作为具有挑战性的新型基准数据集的潜力。数据集与代码已公开于https://github.com/NKU-HLT/WildElder。