{"references": ["DataTang. 2020. aidatatang_200zh, a free Chinese Mandarin speech corpus by Beijing DataTang Technology Co., Ltd ( www.datatang.com ). Online; accessed 08-Oct-2022.", "Hui Bu, Jiayu Du, Xingyu Na, Bengu Wu, and Hao Zheng. 2017. Aishell-1: An open-source mandarin speech corpus and a speech recognition baseline. In 2017 20th conference of the oriental chapter of the international coordinating committee on speech databases and speech I/O systems and assessment (O-COCOSDA). IEEE, 1\u20135.", "Xin Xu Shaoji Zhang Ming Li Yao Shi, Hui Bu. 2015. AISHELL-3: A Multi-speaker Mandarin TTS Corpus and the Baselines. https://arxiv.org/abs/2010.11567", "Ltd Surfing Technology Beijing Co. 2018. ST-CMDS-20170001_1, Free ST Chinese Mandarin Corpus. http://www.openslr.org/38/. (2018). Online; accessed 08-Oct- 2022.", "Ltd Magic Data Technology Co. 2019. MAGICDATA Mandarin Chinese Read Speech Corpus. http://www.openslr.org/68/. (2019). Online; accessed 08-Oct- 2022", "Joel Frank and Lea Sch\u00f6nherr. 2021. WaveFake: A Data Set to Facilitate Audio Deepfake Detection. In Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2). https://openreview.net/forum? id=74TZg9gsO8W", "Haoxin Ma, Jiangyan Yi, Chenglong Wang, Xinrui Yan, Jianhua Tao, Tao Wang, Shiming Wang, Le Xu, and Ruibo Fu. 2022. FAD: A Chinese Dataset for Fake Audio Detection. arXiv preprint arXiv:2207.12308 (2022).", "KinglittleQ. 2018. GST-Tacotron. https://github.com/KinglittleQ/GST-Tacotron. (2018). Online; accessed 09-Oct-2022.", "Chung-Ming Chien, Jheng-Hao Lin, Chien-yu Huang, Po-chun Hsu, and Hung-yi Lee. 2021. Investigating on Incorporating Pretrained and Learnable Speaker Representations for Multi-Speaker Multi-Style Text-to-Speech. In ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 8588\u20138592. https://doi.org/10.1109/ICASSP39728.2021.9413880", "UEhQZXI. 2021. vits_chinese. https://github.com/UEhQZXI/2021. (2021). Online; accessed 09-Oct-2022.", "Yinghao Aaron Li, Ali Zare, and Nima Mesgarani. 2021. Starganv2-vc: A diverse, unsupervised, non-parallel framework for natural-sounding voice conversion. arXiv preprint arXiv:2107.10394 (2021).", "Bac Nguyen and Fabien Cardinaux. 2021. NVC-Net: End-to-End Adversarial Voice Conversion. arXiv preprint arXiv:2106.00992 (2021).", "Zhenyu Zhang, Yewei Gu, Xiaowei Yi, and Xianfeng Zhao. 2021. FMFCCA: A Challenging Mandarin Dataset for Synthetic Speech Detection. CoRR abs/2110.09441 (2021). arXiv:2110.09441 https://arxiv.org/abs/2110.09441", "Massimiliano Todisco, Xin Wang, Ville Vestman, Md Sahidullah, Hector Delgado, Andreas Nautsch, Junichi Yamagishi, Nicholas Evans, Tomi Kinnunen, and Kong Aik Lee. 2019. ASVspoof 2019: Future Horizons in Spoofed and Fake Audio Detection. arXiv preprint arXiv:1904.05441 (2019)."]}

Deepfake cross-lingual evaluation dataset (DECRO) is constructed to evaluate the influence of language differences on deepfake detection. If you use DECRO dataset for deepfake detection, please cite the paper "Transferring Audio Deepfake Detection Capability across Languages" published in www'23.