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.

本研究旨在使用生成型语言模型与 TTS 模型，提供合适的发音特征，以更好地表达不同的说话风格和对话语境，从而提升语音合成的表现。