  Pause insertion, also known as phrase break prediction and phrasing, is an
essential part of TTS systems because proper pauses with natural duration
significantly enhance the rhythm and intelligibility of synthetic speech.
However, conventional phrasing models ignore various speakers' different styles
of inserting silent pauses, which can degrade the performance of the model
trained on a multi-speaker speech corpus. To this end, we propose more powerful
pause insertion frameworks based on a pre-trained language model. Our approach
uses bidirectional encoder representations from transformers (BERT) pre-trained
on a large-scale text corpus, injecting speaker embedding to capture various
speaker characteristics. We also leverage duration-aware pause insertion for
more natural multi-speaker TTS. We develop and evaluate two types of models.
The first improves conventional phrasing models on the position prediction of
respiratory pauses (RPs), i.e., silent pauses at word transitions without
punctuation. It performs speaker-conditioned RP prediction considering
contextual information and is used to demonstrate the effect of speaker
information on the prediction. The second model is further designed for
phoneme-based TTS models and performs duration-aware pause insertion,
predicting both RPs and punctuation-indicated pauses (PIPs) that are
categorized by duration. The evaluation results show that our models improve
the precision and recall of pause insertion and the rhythm of synthetic speech.
