论文标题
低资源多语言和零击的多言式tts
Low-Resource Multilingual and Zero-Shot Multispeaker TTS
论文作者
论文摘要
尽管文本到语音的神经方法（TTS）在模拟多个扬声器方面已经取得了巨大进步，即使在零拍设置中，这些方法所需的数据量通常对于世界上6,000多种语言中的绝大多数人来说都是不可行的。在这项工作中，我们将零拍的语音克隆和多语言低资源TT的任务汇总在一起。使用语言不可知论的元学习（LAML）程序和对TTS编码器的修改，我们表明，系统可以使用仅5分钟的培训数据来学习讲新语言，同时保留在新学习的语言中推断出甚至是看不见的说话者的语音的能力。我们通过客观指标以及人类研究表明了我们提出的方法在与目标扬声器的可理解性，自然性和相似性方面的成功，并提供我们的代码和训练有素的模型开源。
While neural methods for text-to-speech (TTS) have shown great advances in modeling multiple speakers, even in zero-shot settings, the amount of data needed for those approaches is generally not feasible for the vast majority of the world's over 6,000 spoken languages. In this work, we bring together the tasks of zero-shot voice cloning and multilingual low-resource TTS. Using the language agnostic meta learning (LAML) procedure and modifications to a TTS encoder, we show that it is possible for a system to learn speaking a new language using just 5 minutes of training data while retaining the ability to infer the voice of even unseen speakers in the newly learned language. We show the success of our proposed approach in terms of intelligibility, naturalness and similarity to target speaker using objective metrics as well as human studies and provide our code and trained models open source.
