使用离散自监督单元在波形域中进行说话风格转换
Speaking Style Conversion in the Waveform Domain Using Discrete Self-Supervised Units
AI总结：
提出DISSC轻量级无文本语音转换方法，利用预训练自监督离散单元编码并仅用重建任务训练，实现节奏、音高和音色转换，无需配对数据且显著优于基线。
摘要：
我们提出了DISSC，一种新颖的轻量级方法，能够以无文本的方式将录音的节奏、音高轮廓和音色转换为目标说话人。与DISSC不同，大多数语音转换（VC）方法主要关注音色，而忽略了人们独特的说话风格（韵律）。所提出的方法使用预训练的自监督模型将语音编码为离散单元，这使得训练变得简单、高效且快速。所有转换模块仅在重建类任务上进行训练，因此适用于无需配对数据的任意到多人VC。我们为该设置引入了一套定量和定性评估指标，并实证表明DISSC显著优于所评估的基线方法。代码和样本可在https://pages.cs.huji.ac.il/adiyoss-lab/dissc/获取。
英文摘要：
We introduce DISSC, a novel, lightweight method that converts the rhythm, pitch contour and timbre of a recording to a target speaker in a textless manner. Unlike DISSC, most voice conversion (VC) methods focus primarily on timbre, and ignore people's unique speaking style (prosody). The proposed approach uses a pretrained, self-supervised model for encoding speech to discrete units, which makes it simple, effective, and fast to train. All conversion modules are only trained on reconstruction like tasks, thus suitable for any-to-many VC with no paired data. We introduce a suite of quantitative and qualitative evaluation metrics for this setup, and empirically demonstrate that DISSC significantly outperforms the evaluated baselines. Code and samples are available at https://pages.cs.huji.ac.il/adiyoss-lab/dissc/.
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。
