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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2406.07816 |
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| _version_ | 1866910482899664896 |
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| author | Zhang, Lin Wang, Xin Cooper, Erica Diez, Mireia Landini, Federico Evans, Nicholas Yamagishi, Junichi |
| author_facet | Zhang, Lin Wang, Xin Cooper, Erica Diez, Mireia Landini, Federico Evans, Nicholas Yamagishi, Junichi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_07816 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Spoof Diarization: "What Spoofed When" in Partially Spoofed Audio Zhang, Lin Wang, Xin Cooper, Erica Diez, Mireia Landini, Federico Evans, Nicholas Yamagishi, Junichi Audio and Speech Processing Computation and Language Sound 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. |
| title | Spoof Diarization: "What Spoofed When" in Partially Spoofed Audio |
| topic | Audio and Speech Processing Computation and Language Sound |
| url | https://arxiv.org/abs/2406.07816 |