迈向自然化语音转换：NaturalVoices 数据集及其自动处理流水线
Towards Naturalistic Voice Conversion: NaturalVoices Dataset with an Automatic Processing Pipeline
AI总结：
针对语音转换研究缺乏自然自发语音数据的问题，本文提出自动处理流水线并构建包含超3800小时播客语音的NaturalVoices数据集，实验证明其能为语音转换及更广泛的语音生成任务提供自然且富有表现力的数据。
摘要：
语音转换（VC）研究传统上依赖照稿朗读或表演式语音，缺乏真实对话中的自然自发性。由于用于 VC 的自然语音数据有限，本研究聚焦于填补这一空白。我们提出一种新的数据采集流水线，用于发布一个面向 VC 的自然语音数据集，名为 NaturalVoices。该流水线利用最新的深度学习方法，从原始播客数据中提取语音中的丰富信息，如情感和信噪比（SNR），并具有灵活性和易用性。NaturalVoices 是一个大规模、自发、富有表现力和情感色彩的语音数据集，包含超过 3,800 小时的语音，来源于 MSP-Podcast 数据集中的原始播客。客观和主观评估证明了使用我们的流水线为 VC 提供自然且富有表现力的数据的有效性，表明 NaturalVoices 在更广泛的语音生成任务中具有潜力。
英文摘要：
Voice conversion (VC) research traditionally depends on scripted or acted speech, which lacks the natural spontaneity of real-life conversations. While natural speech data is limited for VC, our study focuses on filling in this gap. We introduce a novel data-sourcing pipeline that makes the release of a natural speech dataset for VC, named NaturalVoices. The pipeline extracts rich information in speech such as emotion and signal-to-noise ratio (SNR) from raw podcast data, utilizing recent deep learning methods and providing flexibility and ease of use. NaturalVoices marks a large-scale, spontaneous, expressive, and emotional speech dataset, comprising over 3,800 hours speech sourced from the original podcasts in the MSP-Podcast dataset. Objective and subjective evaluations demonstrate the effectiveness of using our pipeline for providing natural and expressive data for VC, suggesting the potential of NaturalVoices for broader speech generation tasks.
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。
