RawTFNet: A Lightweight CNN Architecture for Speech Anti-spoofing

Yang Xiao; Ting Dang; Rohan Kumar Das

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arXiv · 2507.08227

RawTFNet: A Lightweight CNN Architecture for Speech Anti-spoofing

Yang Xiao·Ting Dang·Rohan Kumar Das

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Abstract

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.

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Yang Xiao, Ting Dang, Rohan Kumar Das. 2025-07-11. RawTFNet: A Lightweight CNN Architecture for Speech Anti-spoofing. https://arxiv.org/abs/2507.08227

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