End-to-End Dual-Branch Network Towards Synthetic Speech Detection
Author:
Affiliation:
1. Engineering Research Center of Digital Forensics, Ministry of Education, Nanjing University of Information Science & Technology, Nanjing, China
2. Center for Machine Vision and Signal Analysis, University of Oulu, Oulu, Finland
Funder
National Natural Science Foundation of China
Academy of Finland for ICT 2023 project TrustFace
Infotech Oulu
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Applied Mathematics,Electrical and Electronic Engineering,Signal Processing
Link
http://xplorestaging.ieee.org/ielx7/97/10036333/10082951.pdf?arnumber=10082951
Reference35 articles.
1. Modified Magnitude-Phase Spectrum Information for Spoofing Detection
2. Tandem Assessment of Spoofing Countermeasures and Automatic Speaker Verification: Fundamentals
3. Significance of Subband Features for Synthetic Speech Detection
4. Unsupervised domain adaptation by backpropagation;ganin;Proc Int Conf Mach Learn,2015
5. Generalized end-to-end detection of spoofing attacks to automatic speaker recognizers
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1. One-Class Neural Network With Directed Statistics Pooling for Spoofing Speech Detection;IEEE Transactions on Information Forensics and Security;2024
2. Spoof Detection using Voice Contribution on LFCC features and ResNet-34;2023 18th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP);2023-11-27
3. Vocoder Detection of Spoofing Speech Based on GAN Fingerprints and Domain Generalization;ACM Transactions on Multimedia Computing, Communications, and Applications;2023-10-28
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