While speaker adaptation for end-to-end speech synthesis using speaker
embeddings can produce good speaker similarity for speakers seen during
training, there remains a gap for zero-shot adaptation to unseen speakers. We
investigate multi-speaker modeling for end-to-end text-to-speech synthesis and
study the effects of different types of state-of-the-art neural speaker
embeddings on speaker similarity for unseen speakers. Learnable dictionary
encoding-based speaker embeddings with angular softmax loss can improve equal
error rates over x-vectors in a speaker verification task; these embeddings
also improve speaker similarity and naturalness for unseen speakers when used
for zero-shot adaptation to new speakers in end-to-end speech synthesis.

研究了使用多说话人建模中的神经发音人嵌入对零样本适应的影响，发现使用可学习字典编码的说话人嵌入，能够在说话人验证任务中提高等误差率，在未知说话人使用时提高零样本适应性，并提高端到端语音合成的说话人相似性和自然度。

使用最先进的神经说话人嵌入进行零样本多说话人文本转语音

Zero-Shot Multi-Speaker Text-To-Speech with State-of-the-art Neural  Speaker Embeddings

Thanks to improvements in machine learning techniques, including deep
learning, speech synthesis is becoming a machine learning task. To accelerate
speech synthesis research, we are developing Japanese voice corpora reasonably
accessible from not only academic institutions but also commercial companies.
In 2017, we released the JSUT corpus, which contains 10 hours of reading-style
speech uttered by a single speaker, for end-to-end text-to-speech synthesis.
For more general use in speech synthesis research, e.g., voice conversion and
multi-speaker modeling, in this paper, we construct the JVS corpus, which
contains voice data of 100 speakers in three styles (normal, whisper, and
falsetto). The corpus contains 30 hours of voice data including 22 hours of
parallel normal voices. This paper describes how we designed the corpus and
summarizes the specifications. The corpus is available at our project page.

本文介绍了日语语音库 JVS 的构建过程和规格，并概述了其在话音合成、语音转换和多人训练等方面的研究应用。