End-to-end dual-branch network towards synthetic speech detection
|Author:||Ma, Kaijie1,2,3; Feng, Yifan1,2,3; Chen, Beijing1,2,3;|
1Engineering Research Center of Digital Forensics, Ministry of Education, Nanjing University of Information Science & Technology, Nanjing, China
2School of Computer Science, Nanjing University of Information Science &Technology, Nanjing, China
3Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science & Technology, Nanjing, China
4Center for Machine Vision and Signal Analysis, University of Oulu, Oulu, Finland
|Online Access:||PDF Full Text (PDF, 0.6 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2023061555562
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2023-06-15
Synthetic speech attacks bring more threats to Automatic Speaker Verification (ASV) systems, thus many synthetic speech detection (SSD) systems have been proposed to help the ASV system resist synthetic speech attacks. However, existing SSD systems still lack the generalization ability for the attacks generated by unknown synthesis algorithms. This letter proposes an end-to-end ensemble system, namely Dual-Branch Network, in which linear frequency cepstral coefficients (LFCC) and constant Q transform (CQT) are used as the input of two branches respectively. In addition, four fusion strategies are compared for the fusion of two branches to obtain an optimal one; multi-task learning and convolutional block attention module (CBAM) are introduced into the Dual-Branch Network to help the network learn the common forgery features from different forgery types of speech and enhance the representation power of learned features. Experimental results on the ASVspoof 2019 logical access (LA) dataset demonstrate that the proposed system outperforms existing state-of-the-art systems on both t-DCF and EER scores and has good generalization for unknown forgery types of synthetic speech.
IEEE signal processing letters
|Pages:||359 - 363|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
This work was supported in part by the National Natural Science Foundation of China under Grant 62072251, in part by the Academy of Finland for ICT 2023 project TrustFace under Grant 345948, and in part by Infotech Oulu.
|Academy of Finland Grant Number:||
345948 (Academy of Finland Funding decision)
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