NaturalVoices：一个用于语音转换的大规模、自发且富有情感的播客数据集
NaturalVoices: A Large-Scale, Spontaneous and Emotional Podcast Dataset for Voice Conversion
AI总结：
针对语音转换领域缺乏大规模真实情感语音资源的问题，发布了首个大规模自发播客数据集NaturalVoices，包含5049小时录音及多维度自动标注，并开源数据处理流水线，实验证明其能促进稳健泛化模型开发并揭示现有架构局限。
摘要：
日常语音传达的远不止词汇，它反映了我们是谁、我们的感受以及我们互动的周围环境。然而，大多数现有的语音数据集都是表演性的，规模有限，未能捕捉到现实生活交流中丰富的表现力。随着大型神经网络的兴起，出现了几个大规模语音语料库，并被广泛应用于各种语音处理任务中。但是，语音转换（VC）领域仍然缺乏适合建模自然韵律和情感的大规模、富有表现力的现实生活语音资源。为了填补这一空白，我们发布了NaturalVoices（NV），这是首个专门为情感感知语音转换设计的大规模自发播客数据集。它包含5,049小时的自发播客录音，并带有自动标注的情感（基于类别和属性）、语音质量、转录文本、说话人身份和声音事件。该数据集捕捉了数千名说话人、多样化主题和自然说话风格之间富有表现力的情感变化。我们还提供了一个带有模块化标注工具和灵活过滤的开源流水线，使研究人员能够为广泛的VC任务构建定制子集。实验表明，NaturalVoices支持开发稳健且具泛化能力的VC模型，能够产生自然、富有表现力的语音，同时揭示了当前架构在应用于大规模自发数据时的局限性。这些结果表明，NaturalVoices既是推进语音转换领域的宝贵资源，也是一个具有挑战性的基准。数据集获取地址：https://huggingface.co/JHU-SmileLab
英文摘要：
Everyday speech conveys far more than words, it reflects who we are, how we feel, and the circumstances surrounding our interactions. Yet, most existing speech datasets are acted, limited in scale, and fail to capture the expressive richness of real-life communication. With the rise of large neural networks, several large-scale speech corpora have emerged and been widely adopted across various speech processing tasks. However, the field of voice conversion (VC) still lacks large-scale, expressive, and real-life speech resources suitable for modeling natural prosody and emotion. To fill this gap, we release NaturalVoices (NV), the first large-scale spontaneous podcast dataset specifically designed for emotion-aware voice conversion. It comprises 5,049 hours of spontaneous podcast recordings with automatic annotations for emotion (categorical and attribute-based), speech quality, transcripts, speaker identity, and sound events. The dataset captures expressive emotional variation across thousands of speakers, diverse topics, and natural speaking styles. We also provide an open-source pipeline with modular annotation tools and flexible filtering, enabling researchers to construct customized subsets for a wide range of VC tasks. Experiments demonstrate that NaturalVoices supports the development of robust and generalizable VC models capable of producing natural, expressive speech, while revealing limitations of current architectures when applied to large-scale spontaneous data. These results suggest that NaturalVoices is both a valuable resource and a challenging benchmark for advancing the field of voice conversion. Dataset is available at: https://huggingface.co/JHU-SmileLab
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。
