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

Automatic Prosody Prediction for Chinese Speech Synthesis using BLSTM-RNN and Embedding Features