We present an unsupervised approach that converts the input speech of any
individual into audiovisual streams of potentially-infinitely many output
speakers. Our approach builds on simple autoencoders that project out-of-sample
data onto the distribution of the training set. We use Exemplar Autoencoders to
learn the voice, stylistic prosody, and visual appearance of a specific target
exemplar speech. In contrast to existing methods, the proposed approach can be
easily extended to an arbitrarily large number of speakers and styles using
only 3 minutes of target audio-video data, without requiring {\em any} training
data for the input speaker. To do so, we learn audiovisual bottleneck
representations that capture the structured linguistic content of speech. We
outperform prior approaches on both audio and video synthesis, and provide
extensive qualitative analysis on our project page --
this https URL

该论文提出了一种无监督方法，可以将任何个人的输入语音转换为无限数量的输出扬声器的音频可视化流，并使用先例自编码器学习特定目标样本语音的声音、风格韵律和视觉外观，它可以很容易地扩展到任意数量的扬声器和风格，而无需对输入扬声器进行任何训练数据。