  Prosody affects the naturalness and intelligibility of speech. However,
automatic prosody prediction from text for Chinese speech synthesis is still a
great challenge and the traditional conditional random fields (CRF) based
method always heavily relies on feature engineering. In this paper, we propose
to use neural networks to predict prosodic boundary labels directly from
Chinese characters without any feature engineering. Experimental results show
that stacking feed-forward and bidirectional long short-term memory (BLSTM)
recurrent network layers achieves superior performance over the CRF-based
method. The embedding features learned from raw text further enhance the
performance.
