标题： ALO-VC：任意到任意低延迟一次性语音转换 显示英文标题

标题： ALO-VC: Any-to-any Low-latency One-shot Voice Conversion

摘要： 本文提出了一种名为ALO-VC的非并行低延迟一次性音素后验图（PPGs）为基础的声音转换方法。 ALO-VC仅使用目标说话人的一个语音片段即可实现任意到任意的声音转换，且仅需47.5毫秒的未来预测时间。 所提出的混合信号处理和机器学习管道结合了一个预训练的说话人编码器、一个用于预测转换语音韵律的音高预测器以及位置编码以传达音素的位置信息。 我们介绍了两种系统版本：ALO-VC-R，它使用预训练的d-vector说话人编码器；以及ALO-VC-E，它通过使用ECAPA-TDNN说话人编码器来提升性能。 实验结果表明，ALO-VC-R和ALO-VC-E在VCTK数据集和两个域外数据集上均能达到与非因果基线系统相当的性能。 此外，这两种提议的系统均可部署在单个CPU核心上，延迟为55毫秒，实时因子为0.78。 我们的演示可在线访问。 显示英文摘要

摘要： This paper presents ALO-VC, a non-parallel low-latency one-shot phonetic posteriorgrams (PPGs) based voice conversion method. ALO-VC enables any-to-any voice conversion using only one utterance from the target speaker, with only 47.5 ms future look-ahead. The proposed hybrid signal processing and machine learning pipeline combines a pre-trained speaker encoder, a pitch predictor to predict the converted speech's prosody, and positional encoding to convey the phoneme's location information. We introduce two system versions: ALO-VC-R, which uses a pre-trained d-vector speaker encoder, and ALO-VC-E, which improves performance using the ECAPA-TDNN speaker encoder. The experimental results demonstrate both ALO-VC-R and ALO-VC-E can achieve comparable performance to non-causal baseline systems on the VCTK dataset and two out-of-domain datasets. Furthermore, both proposed systems can be deployed on a single CPU core with 55 ms latency and 0.78 real-time factor. Our demo is available online.