RawTFNet: A Lightweight CNN Architecture for Speech Anti-spoofing
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866912475992031232 |
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| author | Xiao, Yang Dang, Ting Das, Rohan Kumar |
| author_facet | Xiao, Yang Dang, Ting Das, Rohan Kumar |
| contents | Automatic speaker verification (ASV) systems are often affected by spoofing attacks. Recent transformer-based models have improved anti-spoofing performance by learning strong feature representations. However, these models usually need high computing power. To address this, we introduce RawTFNet, a lightweight CNN model designed for audio signals. The RawTFNet separates feature processing along time and frequency dimensions, which helps to capture the fine-grained details of synthetic speech. We tested RawTFNet on the ASVspoof 2021 LA and DF evaluation datasets. The results show that RawTFNet reaches comparable performance to that of the state-of-the-art models, while also using fewer computing resources. The code and models will be made publicly available. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_08227 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | RawTFNet: A Lightweight CNN Architecture for Speech Anti-spoofing Xiao, Yang Dang, Ting Das, Rohan Kumar Audio and Speech Processing Sound Automatic speaker verification (ASV) systems are often affected by spoofing attacks. Recent transformer-based models have improved anti-spoofing performance by learning strong feature representations. However, these models usually need high computing power. To address this, we introduce RawTFNet, a lightweight CNN model designed for audio signals. The RawTFNet separates feature processing along time and frequency dimensions, which helps to capture the fine-grained details of synthetic speech. We tested RawTFNet on the ASVspoof 2021 LA and DF evaluation datasets. The results show that RawTFNet reaches comparable performance to that of the state-of-the-art models, while also using fewer computing resources. The code and models will be made publicly available. |
| title | RawTFNet: A Lightweight CNN Architecture for Speech Anti-spoofing |
| topic | Audio and Speech Processing Sound |
| url | https://arxiv.org/abs/2507.08227 |