Research progress on speech deepfake and its detection techniques

Author:

Xu Yuxiong, ,Li Bin,Tan Shunquan,Huang Jiwu, , ,

Publisher

Aerospace Information Research Institute, Chinese Academy of Sciences

Reference118 articles.

1. Aihara R, Takiguchi T and Ariki Y. 2013. Individuality-preserving voice conversion for articulation disorders using locality-constrained NMF//Proceedings of the 4th Workshop on Speech and Language Processing for Assistive Technologies. Grenoble, France: Association for Computational Linguistics:3-8

2. Almutairi Z and Elgibreen H. 2022. A review of modern audio deepfake detection methods: challenges and future directions. Algorithms, 15(5): #155 [DOI: 10.3390/a15050155]

3. Arif T, Javed A, Alhameed M, Jeribi F and Tahir A. 2021. Voice spoofing countermeasure for logical access attacks detection. IEEE Access, #9: 162857-162868 [DOI: 10.1109/ACCESS.2021.3133134]

4. Arik S Ö, Chrzanowski M, Coates A, Diamos G, Gibiansky A, Kang Y G, Li X, Miller J, Ng A, Raiman J, Sengupta S and Shoeybi M. 2017a. Deep voice: real-time neural text-to-speech//Proceedings of the 34th International Conference on Machine Learning. Sydney, Australia: JMLR.org:195-204 [DOI: 10.5555/3305381.3305402]

5. Arik S Ö, Diamos G, Gibiansky A, Miller J, Peng K N, Ping W, Raiman J and Zhou Y Q. 2017b. Deep voice 2: multi-speaker neural text-to-speech//Proceedings of the 31st International Conference on Neural Information Processing Systems. Long Beach, USA: Curran Associates Inc.:2966-2974 [DOI: 10.5555/3294996.3295056]

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