Appropriate prosody is critical for successful spoken communication. Contextual word embeddings are proven to be helpful in predicting prosody but do not allow for choosing between plausible prosodic renditions. Reference-based TTS models attempt to address this by conditioning speech generation on a reference speech sample. These models can generate expressive speech but this requires finding an appropriate reference. Sufficiently large generative language models have been used to solve various language-related tasks. We explore whether such models can be used to suggest appropriate prosody for expressive TTS. We train a TTS model on a non-expressive corpus and then prompt the language model to suggest changes to pitch, energy and duration. The prompt can be designed for any task and we prompt the model to make suggestions based on target speaking style and dialogue context. The proposed method is rated most appropriate in 49.9\% of cases compared to 31.0\% for a baseline model.

翻译：适当的韵律对于成功的口语交流至关重要。上下文词嵌入已被证明有助于预测韵律，但无法在多种可行的韵律表达之间进行选择。基于参考的文本转语音模型试图通过将语音生成条件化于参考语音样本来解决此问题。这类模型能够生成富有表现力的语音，但需要找到适当的参考样本。足够大的生成式语言模型已被用于解决各种与语言相关的任务。我们探索了此类模型是否可用于为富有表现力的文本转语音建议合适的韵律。我们在非表现力语料库上训练了一个文本转语音模型，然后提示语言模型对音高、能量和时长提出修改建议。该提示可针对任意任务进行设计，我们根据目标说话风格和对话上下文提示模型提出建议。与基线模型的31.0%相比，所提方法在49.9%的案例中被评为最合适。