论文标题
语音过滤器：使用语音转换作为后处理模块，很少发声的文本到语音扬声器改编
Voice Filter: Few-shot text-to-speech speaker adaptation using voice conversion as a post-processing module
论文作者
论文摘要
最先进的文本到语音（TTS）系统需要几个小时的记录语音数据才能产生高质量的合成语音。当使用减少培训数据时，标准TTS模型会遭受语音质量和可理解性降解，从而使低资源TTS系统有问题。在本文中，我们提出了一种名为“语音过滤器”的新型非常低的资源TTS方法，该方法的使用不足目标扬声器的语音一分钟。它使用语音转换（VC）作为附加到预先存在的高质量TTS系统的后处理模块，并标志着现有TTS范式的概念转移，将几个弹出的TTS问题作为VC任务。此外，我们建议使用可控制的TTS系统来创建平行的语音语料库来促进VC任务。结果表明，语音过滤器优于最先进的语音综合技术在一分钟的语音上，在各种声音上的语音中，在客观和主观的指标方面，语音过滤器优于最先进的语音综合技术，同时在30倍的数据上与TTS模型具有竞争力。
State-of-the-art text-to-speech (TTS) systems require several hours of recorded speech data to generate high-quality synthetic speech. When using reduced amounts of training data, standard TTS models suffer from speech quality and intelligibility degradations, making training low-resource TTS systems problematic. In this paper, we propose a novel extremely low-resource TTS method called Voice Filter that uses as little as one minute of speech from a target speaker. It uses voice conversion (VC) as a post-processing module appended to a pre-existing high-quality TTS system and marks a conceptual shift in the existing TTS paradigm, framing the few-shot TTS problem as a VC task. Furthermore, we propose to use a duration-controllable TTS system to create a parallel speech corpus to facilitate the VC task. Results show that the Voice Filter outperforms state-of-the-art few-shot speech synthesis techniques in terms of objective and subjective metrics on one minute of speech on a diverse set of voices, while being competitive against a TTS model built on 30 times more data.
