I am Xin Wang, a postdoc researcher at Yamagishi Lab, National Institute of Informatics (NII), Japan.
I am studying speech processing and machine learning, and I learned a lot from my supervisors at NII and USTC and many colleagues around the world.
My Researchmap site and Google Scholar page. Researchmap is the official website for researchers in Japan to upload research outcomes.
Here is my Resume.
Slides for SPSC webinar presentation on “Using vocoders to create spoofed training data for speech spoofing countermeasures” can be found in Slide page. This is a presentation for ICASSP 2023 paper. The code, pre-trained models, and other resources are available at this repo.
The materials tutorials (Jupyter notebook) on speaker anonymization is available at Slide page.
The materials for ICASSP short course on neural vocoders are available on Google colab. The old contents are re-edited, and new contents are available (including NSF-HiFiGAN).
The slides for the talk on speech anti-spoofing and anonymization are uploaded. Two Speech Security Issues after Speech Synthesis Boom, 2021 Dec, a talk given at CCF. download from here.
The ASVspoof 2021 Workshop, an official Interspeech 2021 satellite event, will be held online in the form of a Zoom Webinar on September 16th, 2021.
The tentative technical program is available from the ASVspoof website
The registration link is here
Tutorials on neural vocoders for SPCC 2021 have been uploade online
Slides are available on Slideshare ;
Hands-on materials on some vocoders (WaveNet, WaveGlow, Blow, NSF) are added to git repo. Please check the tutorials folder.
Pytorch project is updated https://github.com/nii-yamagishilab/project-NN-Pytorch-scripts:
Accumulated commits over the last year;
Projects on ASVspoof: many pre-trained models and training recipes;
New slides have been uploaded:
Interspeech 2020 presentation for Cyclic-noise NSF: PPT and PDF slides
Odyssey tutorial presentation: PDF and PPT slides
Repositories from my personal git account have been moved to https://github.com/nii-yamagishilab, including:
CURRENNT and related source codes: https://github.com/nii-yamagishilab/project-CURRENNT-public
CURRENNT scripts for model training: https://github.com/nii-yamagishilab/project-CURRENNT-scripts
Tutorial materials on Pytorch NSF models are uploaded. They are based on Jupyter notebooks and can be run on laptop with CPU;
Pytorch re-implementation of NSF models is available: https://github.com/nii-yamagishilab/project-NN-Pytorch-scripts
Please send email to wangxin ~a~t~ nii dot ac dot jp.