Zhengxi Liu

刘正曦

  • Undergraduate in Sun Yat-sen University
  • Major in information and computing science (2018-2022)
  • Interested in speech technology, web development and high performance programming
  • Full stack developer of MistGPU, and working on mobileboard💪
  • My Github (270+ followers, 1k+ stars)
  • contact me
  • 中文版

  • Research Interests

  • Efficient and high quality neural vocoder
  • Expressive TTS acoustic model
  • Educations

  • 09/2018 - 06/2022, Sun Yat-sen University, Guangzhou, China
    1. B.S. in Information and Computing Science

    Publications

  • Zhengxi Liu and Yanmin Qian, Basis-MelGAN: Efficient Neural Vocoder Based on Audio Decomposition, to appear in InterSpeech 2021.
    1. paper, code
    2. Proposed a GAN-based neural vocoder model, which has a novel architecture, using TasNet basis matrix as a part of the model, and have shown this design makes the improvement of inference speed and audio quality.

    Awards

  • 1st prize and best performance award on ASC20-21
    1. winners site
    2. We won the highest performance award and achieved the third best result in the total score. I am mainly responsible for the optimization of AI challenge in our team.

    Experiences

  • Research Intern, AI Platform, HuYa Inc., Guangzhou, 05/2021-07/2021
    1. Mentor: Shiyin Kang, Deyi Tuo
    2. Focus on efficient neural vocoder.
  • Research Intern, Lightspeed & Quantum Studios Group, Tencent Inc., Shenzhen, 03/2020-05/2021
    1. Mentor: Chengzhu Yu and Shuai Wang
    2. Focus on efficient neural vocoder and multi-style TTS model.
  • Research Intern, Speech team, DMAI Inc., Guangzhou, 07/2019-09/2019
    1. Mentor: Xiaodan Liang
    2. Focus on multi-speaker TTS model. Explore the application of lifelong learning in multi-speaker TTS.
  • Research Intern, HCP lab, Sun Yat-sen University, Guangzhou, 10/2018-10/2019
    1. Mentor: Xiaodan Liang
    2. Acquisition the knowledge of speech synthesis and deep learning.

    Skills

  • Python, C/C++, JavaScript, Pytorch, Docker, MySQL, Flask.