Creating realistic and natural-sounding synthetic speech remains a big
challenge for voice identities unseen during training. As there is growing
interest in synthesizing voices of new speakers, here we investigate the
ability of normalizing flows in text-to-speech (TTS) and voice conversion (VC)
modes to extrapolate from speakers observed during training to create unseen
speaker identities. Firstly, we create an approach for TTS and VC, and then we
comprehensively evaluate our methods and baselines in terms of intelligibility,
naturalness, speaker similarity, and ability to create new voices. We use both
objective and subjective metrics to benchmark our techniques on 2 evaluation
tasks: zero-shot and new voice speech synthesis. The goal of the former task is
to measure the precision of the conversion to an unseen voice. The goal of the
latter is to measure the ability to create new voices. Extensive evaluations
demonstrate that the proposed approach systematically allows to obtain
state-of-the-art performance in zero-shot speech synthesis and creates various
new voices, unobserved in the training set. We consider this work to be the
first attempt to synthesize new voices based on mel-spectrograms and
normalizing flows, along with a comprehensive analysis and comparison of the
TTS and VC modes.

通过归一化流（normalizing flows）实现从训练时未见过的声音身份合成逼真、自然的合成语音的研究中，我们创建了一种文本转语音（TTS）和语音转换（VC）的方法，并使用客观和主观指标来评估技术在零样本和新声音语音合成任务中的性能，实验证明该方法能够在零样本语音合成和创造未在训练集中出现的多种新声音方面取得最先进的性能。