The goal of expressive Text-to-speech (TTS) is to synthesize natural speech with desired content, prosody, emotion, or timbre, in high expressiveness. Most of previous studies attempt to generate speech from given labels of styles and emotions, which over-simplifies the problem by classifying styles and emotions into a fixed number of pre-defined categories. In this paper, we introduce a new task setting, Contextual TTS (CTTS). The main idea of CTTS is that how a person speaks depends on the particular context she is in, where the context can typically be represented as text. Thus, in the CTTS task, we propose to utilize such context to guide the speech synthesis process instead of relying on explicit labels of styles and emotions. To achieve this task, we construct a synthetic dataset and develop an effective framework. Experiments show that our framework can generate high-quality expressive speech based on the given context both in synthetic datasets and real-world scenarios.
翻译:表达式文本到语音( TTS) 的目标是将自然语言与想要的内容、 手动、 情感或小调合成, 高清晰度。 大部分先前的研究试图从特定风格和情绪标签中生成语言, 过度简化问题, 将样式和情绪分类为固定数量的预定义类别 。 在本文中, 我们引入一个新的任务设置, “ 背景 TTS ” ( CTS ) 。 CTTS 的主要理念是, 一个人说话的方式取决于她所处的特定环境, 其背景通常可以作为文本表示 。 因此, 在 CTTS 任务中, 我们提议利用这种背景来指导语言合成过程, 而不是依赖明确的风格和情绪标签 。 为了完成这项任务, 我们建立一个合成数据集, 并开发一个有效的框架 。 实验显示, 我们的框架能够根据合成数据集和现实世界情景中的特定环境产生高质量的表达式演讲 。