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The task of synthetic speech generation is to generate language content froma given text, then simulating fake human voice.The key factors that determinethe effect of synthetic speech generation mainly include speed of generation,accuracy of word segmentation, naturalness of synthesized speech, etc. Thispaper builds an end-to-end multi-module synthetic speech generation model,including speaker encoder, synthesizer based on Tacotron2, and vocoder based onWaveRNN. In addition, we perform a lot of comparative experiments on differentdatasets and various model structures. Finally, we won the first place in theADD 2023 challenge Track 1.1 with the weighted deception success rate (WDSR) of44.97%.

An End-to-End Multi-Module Audio Deepfake Generation System for ADD Challenge 2023