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%.

翻译：合成语音生成的任务是从给定文本生成语言内容，进而模拟虚假人声。决定合成语音生成效果的关键因素主要包括生成速度、分词准确性、合成语音的自然度等。本文构建了一个端到端的多模块合成语音生成模型，包括说话人编码器、基于Tacotron2的合成器以及基于WaveRNN的声码器。此外，我们在不同数据集和多种模型结构上进行了大量对比实验。最终，我们以44.97%的加权欺骗成功率（WDSR）在ADD 2023挑战赛Track 1.1中荣获第一名。