RawTFNet: A Lightweight CNN Architecture for Speech Anti-spoofing
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.
Keywords
Cite
@article{arxiv.2507.08227,
title = {RawTFNet: A Lightweight CNN Architecture for Speech Anti-spoofing},
author = {Yang Xiao and Ting Dang and Rohan Kumar Das},
journal= {arXiv preprint arXiv:2507.08227},
year = {2025}
}
Comments
Submitted to APSIPA ASC 2025