Spoof Diarization: "What Spoofed When" in Partially Spoofed Audio

Lin Zhang, Xin Wang, Erica Cooper, Mireia Diez, Federico Landini +2 more
6/12/2024
eess.AScs.CLcs.SD

Official code: nii-yamagishilab/PartialSpoof (62 stars, BSD-3-Clause, last commit 2024-07-15).

Abstract

This paper defines Spoof Diarization as a novel task in the Partial Spoof (PS) scenario. It aims to determine what spoofed when, which includes not only locating spoof regions but also clustering them according to different spoofing methods. As a pioneering study in spoof diarization, we focus on defining the task, establishing evaluation metrics, and proposing a benchmark model, namely the Countermeasure-Condition Clustering (3C) model. Utilizing this model, we first explore how to effectively train countermeasures to support spoof diarization using three labeling schemes. We then utilize spoof localization predictions to enhance the diarization performance. This first study reveals the high complexity of the task, even in restricted scenarios where only a single speaker per audio file and an oracle number of spoofing methods are considered. Our code is available at https://github.com/nii-yamagishilab/PartialSpoof.

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Code Implementations(1)

nii-yamagishilab/PartialSpoofOfficial100%
BSD-3-Clause
627Shell, PythonDec 21, 20222 years agoBSD-3-Clause

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Cite this paper

@article{zhang2024spoof,
  title  = {Spoof Diarization: "What Spoofed When" in Partially Spoofed Audio},
  author = {Lin Zhang and Xin Wang and Erica Cooper and Mireia Diez and Federico Landini and Nicholas Evans and Junichi Yamagishi},
  year   = {2024},
  eprint = {2406.07816},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2406.07816v1}
}

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