Polyphone disambiguation aims to capture accurate pronunciation knowledge from natural text sequences for reliable Text-to-speech (TTS) systems. However, previous approaches require substantial annotated training data and additional efforts from language experts, making it difficult to extend high-quality neural TTS systems to out-of-domain daily conversations and countless languages worldwide. This paper tackles the polyphone disambiguation problem from a concise and novel perspective: we propose Dict-TTS, a semantic-aware generative text-to-speech model with an online website dictionary (the existing prior information in the natural language). Specifically, we design a semantics-to-pronunciation attention (S2PA) module to match the semantic patterns between the input text sequence and the prior semantics in the dictionary and obtain the corresponding pronunciations; The S2PA module can be easily trained with the end-to-end TTS model without any annotated phoneme labels. Experimental results in three languages show that our model outperforms several strong baseline models in terms of pronunciation accuracy and improves the prosody modeling of TTS systems. Further extensive analyses demonstrate that each design in Dict-TTS is effective. The code is available at \url{https://github.com/Zain-Jiang/Dict-TTS}.

翻译：摘要：多音字消歧旨在从自然文本序列中捕获准确的发音知识，以构建可靠的文本转语音（TTS）系统。然而，以往的方法需要大量标注训练数据和语言专家的额外投入，这使得将高质量神经TTS系统扩展到领域外的日常对话及全球众多语言变得困难。本文从一个简洁而新颖的角度解决了多音字消歧问题：我们提出Dict-TTS，一种利用在线网站词典（自然语言中现有的先验信息）的语义感知生成式文本转语音模型。具体而言，我们设计了一个语义到发音注意力（S2PA）模块，用于匹配输入文本序列与词典中先验语义之间的语义模式，并获取相应的发音；该S2PA模块可与端到端TTS模型轻松训练，无需任何标注的音素标签。三种语言的实验结果表明，我们的模型在发音准确性上优于多个强基线模型，并提升了TTS系统的韵律建模。进一步的广泛分析证明，Dict-TTS中的每个设计均有效。代码开源地址：\url{https://github.com/Zain-Jiang/Dict-TTS}。