This paper presents an expressive speech synthesis architecture for modeling and controlling the speaking style at a word level. It attempts to learn word-level stylistic and prosodic representations of the speech data, with the aid of two encoders. The first one models style by finding a combination of style tokens for each word given the acoustic features, and the second outputs a word-level sequence conditioned only on the phonetic information in order to disentangle it from the style information. The two encoder outputs are aligned and concatenated with the phoneme encoder outputs and then decoded with a Non-Attentive Tacotron model. An extra prior encoder is used to predict the style tokens autoregressively, in order for the model to be able to run without a reference utterance. We find that the resulting model gives both word-level and global control over the style, as well as prosody transfer capabilities.

翻译：本文展示了用于在单词级别上建模和控制语音样式的表达式语音合成结构。 它试图在两个编码器的帮助下学习语音数据的字级文体和预示表达式。 第一个模型样式, 找到每个单词的样式符号组合, 以音频特性为条件, 第二个输出单词级序列仅以音频信息为条件, 以便与风格信息脱钩。 两个编码器输出与语音编码器编码器输出相匹配, 然后与非惯性调制调子调解。 一个额外的前编码器被用于自动预测样式符号, 以便模型能够在不引用语句的情况下运行。 我们发现, 生成的模型既提供了文字级别, 也提供了对样式的全局控制, 以及 prosody 传输能力 。