  Although word-level prosody modeling in neural text-to-speech (TTS) has been
investigated in recent research for diverse speech synthesis, it is still
challenging to control speech synthesis manually without a specific reference.
This is largely due to lack of word-level prosody tags. In this work, we
propose a novel approach for unsupervised word-level prosody tagging with two
stages, where we first group the words into different types with a decision
tree according to their phonetic content and then cluster the prosodies using
GMM within each type of words separately. This design is based on the
assumption that the prosodies of different type of words, such as long or short
words, should be tagged with different label sets. Furthermore, a TTS system
with the derived word-level prosody tags is trained for controllable speech
synthesis. Experiments on LJSpeech show that the TTS model trained with
word-level prosody tags not only achieves better naturalness than a typical
FastSpeech2 model, but also gains the ability to manipulate word-level prosody.
