  This paper presents an end-to-end high-quality singing voice synthesis (SVS)
system that uses bidirectional encoder representation from Transformers (BERT)
derived semantic embeddings to improve the expressiveness of the synthesized
singing voice. Based on the main architecture of recently proposed VISinger, we
put forward several specific designs for expressive singing voice synthesis.
First, different from the previous SVS models, we use text representation of
lyrics extracted from pre-trained BERT as additional input to the model. The
representation contains information about semantics of the lyrics, which could
help SVS system produce more expressive and natural voice. Second, we further
introduce an energy predictor to stabilize the synthesized voice and model the
wider range of energy variations that also contribute to the expressiveness of
singing voice. Last but not the least, to attenuate the off-key issues, the
pitch predictor is re-designed to predict the real to note pitch ratio. Both
objective and subjective experimental results indicate that the proposed SVS
system can produce singing voice with higher-quality outperforming VISinger.
