Neural text-to-speech synthesis (NTTS) models have shown significant progress
in generating high-quality speech, however they require a large quantity of
training data. This makes creating models for multiple styles expensive and
time-consuming. In this paper different styles of speech are analysed based on
prosodic variations, from this a model is proposed to synthesise speech in the
style of a newscaster, with just a few hours of supplementary data. We pose the
problem of synthesising in a target style using limited data as that of
creating a bi-style model that can synthesise both neutral-style and
newscaster-style speech via a one-hot vector which factorises the two styles.
We also propose conditioning the model on contextual word embeddings, and
extensively evaluate it against neutral NTTS, and neutral concatenative-based
synthesis. This model closes the gap in perceived style-appropriateness between
natural recordings for newscaster-style of speech, and neutral speech synthesis
by approximately two-thirds.

本文提出了一种用于合成新闻播报风格的少量数据语音合成模型，它不仅使用了目标风格的韵律变化，而且在上下文中使用词嵌入进行条件约束，与中立 NTTS 和基于连接的中立合成相比，这个模型缩小了新闻播报风格语音与中立语音合成之间感知的风格适宜性差距约三分之二。