我们可以使用 Common Voice 训练多说话人 TTS 系统吗？
Can we use Common Voice to train a Multi-Speaker TTS system?
AI总结：
本文提出利用非侵入式 MOS 估计器 WV-MOS 自动筛选 Common Voice 中的高质量样本，训练多说话人 GlowTTS 模型，显著提升生成语音质量，为自动构建 TTS 数据集铺路。
摘要：
多说话人文本转语音（TTS）系统的训练依赖于基于高质量录音或有声书策划的数据集。此类数据集往往缺乏说话人多样性且收集成本高昂。作为替代方案，近期研究利用了大规模众包自动语音识别（ASR）数据集的可用性。此类数据集的一个主要问题是存在噪声和/或失真的样本，这会降低 TTS 质量。在本文中，我们提出使用非侵入式平均意见得分（MOS）估计器 WV-MOS 自动选择高质量训练样本。我们展示了该方法在 Common Voice 英语数据集上训练多说话人 GlowTTS 模型的可行性。与使用所有样本训练相比，我们的方法将生成话语的整体质量提高了 1.26 个 MOS 点；与在 LibriTTS 数据集上训练相比，提高了 0.35 个 MOS 点。这为更广泛语言的自动 TTS 数据集策划打开了大门。
英文摘要：
Training of multi-speaker text-to-speech (TTS) systems relies on curated datasets based on high-quality recordings or audiobooks. Such datasets often lack speaker diversity and are expensive to collect. As an alternative, recent studies have leveraged the availability of large, crowdsourced automatic speech recognition (ASR) datasets. A major problem with such datasets is the presence of noisy and/or distorted samples, which degrade TTS quality. In this paper, we propose to automatically select high-quality training samples using a non-intrusive mean opinion score (MOS) estimator, WV-MOS. We show the viability of this approach for training a multi-speaker GlowTTS model on the Common Voice English dataset. Our approach improves the overall quality of generated utterances by 1.26 MOS point with respect to training on all the samples and by 0.35 MOS point with respect to training on the LibriTTS dataset. This opens the door to automatic TTS dataset curation for a wider range of languages.
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。
