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metadata
license: cc-by-nc-4.0
language:
  - en
pretty_name: Deepfake and Spoof Detection meets Neural Audio Codecs
dataset_info:
  features:
    - name: utt_id
      dtype: string
    - name: origin_ds
      dtype: string
    - name: attacker
      dtype: string
    - name: label
      dtype: string
    - name: speaker
      dtype: string
    - name: audio
      dtype: audio
  splits:
    - name: main
      num_bytes: 23569231227.24
      num_examples: 63473
  download_size: 19759387664
  dataset_size: 23569231227.24
configs:
  - config_name: default
    data_files:
      - split: main
        path: data/main-*
tags:
  - deepfake
  - spoof
  - detection

In this spoof detection dataset, the bonafide speech is resynthesized using various popular neural audio codecs, which are used for compression and low-bandwidth transmission of speech signals. The spoofed speech samples we provide are generated with a selection of popular and well performing language model based speech synthesis methods, which utilize the same codecs as the bonafide audios to obtain discretized speech tokens. This takes the artifacts of the codecs out of the equation and lets a deepfake detection model trained on this data focus purely on higher-level patterns to differentiate the genuine human samples from the faked speech samples.

This dataset can be seen as a very challenging extension to the ASVSpoof 5 dataset [1, 2] that aligns the field more closely with upcoming challenges in the real world. A publication associated with this dataset is currently under review.

[1] Xin Wang, Héctor Delgado, Hemlata Tak, Jee-weon Jung, Hye-jin Shim, Massimiliano Todisco, Ivan Kukanov, Xuechen Liu, Md Sahidullah, Tomi Kinnunen, Nicholas Evans, Kong Aik Lee, Junichi Yamagishi, Myeonghun Jeong, Ge Zhu, Yongyi Zang, You Zhang, Soumi Maiti, Florian Lux, Nicolas Müller, Wangyou Zhang, Chengzhe Sun, Shuwei Hou, Siwei Lyu, Sébastien Le Maguer, Cheng Gong, Hanjie Guo, Liping Chen, and Vishwanath Singh. 2024. ASVspoof 5: Design, Collection and Validation of Resources for Spoofing, Deepfake, and Adversarial Attack Detection Using Crowdsourced Speech. In Computer Speech & Language, 2026, 95. Jg., S. 101825. https://www.sciencedirect.com/science/article/pii/S0885230825000506

[2] Xin Wang, Héctor Delgado, Hemlata Tak, Jee-weon Jung, Hye-jin Shim, Massimiliano Todisco, Ivan Kukanov, Xuechen Liu, Md Sahidullah, Tomi Kinnunen, Nicholas Evans, Kong Aik Lee, and Junichi Yamagishi. 2024. ASVspoof 5: Crowdsourced speech data, deepfakes, and adversarial attacks at scale. In ASVspoof Workshop 2024, 2024. 1--8. https://doi.org/10.21437/ASVspoof.2024-1