  The task of synthetic speech generation is to generate language content from
a given text, then simulating fake human voice.The key factors that determine
the effect of synthetic speech generation mainly include speed of generation,
accuracy of word segmentation, naturalness of synthesized speech, etc. This
paper builds an end-to-end multi-module synthetic speech generation model,
including speaker encoder, synthesizer based on Tacotron2, and vocoder based on
WaveRNN. In addition, we perform a lot of comparative experiments on different
datasets and various model structures. Finally, we won the first place in the
ADD 2023 challenge Track 1.1 with the weighted deception success rate (WDSR) of
44.97%.
