This repository hosts the following projects:

Projects for neural source-filter waveform models.

• All the projects include a pre-trained model and a one-click demo script.

• Tutorial on NSF models is available in ../tutorials/b1_neural_vocoder.

• Note that the tutorial chapter_a3_pretrained_vocoders.ipynb includes pre-trained NSF models on VoxCeleb2 dev and other speech datasets .

Xin Wang, Shinji Takaki, and Junichi Yamagishi. Neural Source-Filter Waveform Models for Statistical Parametric Speech Synthesis. IEEE/ACM Transactions on Audio, Speech, and Language Processing 28: 402–415. doi:10.1109/TASLP.2019.2956145. 2020.

Xin Wang, Shinji Takaki, and Junichi Yamagishi. Neural Source-Filter-Based Waveform Model for Statistical Parametric Speech Synthesis. In Proc. ICASSP, 5916–5920. IEEE. doi:10.1109/ICASSP.2019.8682298. 2019.

Xin Wang, and Junichi Yamagishi. Neural Harmonic-plus-Noise Waveform Model with Trainable Maximum Voice Frequency for Text-to-Speech Synthesis. In Proc. SSW, 1–6. ISCA: ISCA. doi:10.21437/SSW.2019-1. 2019.

Xin Wang, and Junichi Yamagishi. Using Cyclic Noise as the Source Signal for Neural Source-Filter-Based Speech Waveform Model. In Proc. Interspeech, 1992–1996. ISCA: ISCA. doi:10.21437/Interspeech.2020-1018. 2020.

Projects for other waveform models, including WaveNet vocoder, WaveGlow, Blow, and iLPCNet.

• All the projects include a pre-trained model and a one-click demo script.

• Tutorial on NSF models is also available in ./tutorials/b1_neural_vocoder.

• Note that the tutorial chapter_a3_pretrained_vocoders.ipynb includes pre-trained HiFiGAN and WaveGlow models on VoxCeleb2 dev and other speech datasets .

Project to compare 36 models, 6 runs per system.

• Pre-trained models, scores, training recipes are all available.

• For statistical analysis, please check the tutorial notebook in ./tutorials/b2_anti_spoofing

Xin Wang, and Junichi Yamagishi. A Comparative Study on Recent Neural Spoofing Countermeasures for Synthetic Speech Detection. In Proc. Interspeech, 4259–4263. doi:10.21437/Interspeech.2021-702. 2021.

Xin Wang, and Junichi Yamagishi. A Practical Guide to Logical Access Voice Presentation Attack Detection. In Frontiers in Fake Media Generation and Detection, 169–214. Springer. doi:10.1007/978-981-19-1524-6_8. 2022.

Project to estimate confidence for speech spoofing countermeasure

Xin Wang, and Junichi Yamagishi. Estimating the Confidence of Speech Spoofing Countermeasure. In Proc. ICASSP, 6372–6376. 2022.

Project to use SSL-based front end

• Pre-trained models and recipes are all available.

• Dependency, pre-trained models, and SSL models will be automatically downloaded using a simple 00_demo.sh

Xin Wang, and Junichi Yamagishi. Investigating Self-Supervised Front Ends for Speech Spoofing Countermeasures. In Proc. Odyssey, 100–106. doi:10.21437/Odyssey.2022-14. 2022.

Project to use active learning to train the model on a large pool data set

• Pre-trained models and recipes are all available.

• Dependency, pre-trained models, and SSL models will be automatically downloaded using a simple 00_demo.sh

Xin Wang, and Junichi Yamagishi. Investigating Active-Learning-Based Training Data Selection for Speech Spoofing Countermeasure. In Proc. SLT, accepted. 2023.

This project uses vocoded spoofed data to train spoofing speech countermeasures. Arxiv link: https://arxiv.org/abs/2210.10570

• Script is available for demonsration on a toy data set

• Vocoders used to create voc.v2, v3, and v4 in the paper are available in this Colab notebook . See more in README

• Links to vocoded datasets are listed in README.

Xin Wang, and Junichi Yamagishi. Spoofed training data for speech spoofing countermeasure can be efficiently created using neural vocoders. Proc. ICASSP, 2023, accepted

This project is for paper

Xin Wang, and Junichi Yamagishi. Can Large-scale vocoded spoofed data improve speech spoofing countermeasure with a self-supervised front end? 2024.

That's all