轻量级CNN模型RawTFNet有效识别语音伪造，兼顾性能与效率。
RawTFNet: A Lightweight CNN Architecture for Speech Anti-spoofing
- 分离时频维度处理音频特征，捕捉合成语音细微差异。
- 在ASVspoof 2021 LA和DF数据集上表现接近顶尖模型。
- 适合资源受限场景的语音反欺骗系统部署。
自动说话人验证（ASV）系统易受欺骗攻击影响。近年来基于变压器的模型通过学习强特征表示提升了反欺骗性能，但通常需要高计算资源。为此，我们提出RawTFNet，一种专为音频信号设计的轻量级卷积神经网络。该模型沿时间与频率维度分离特征处理，有助于捕捉合成语音的细粒度差异。我们在ASVspoof 2021 LA和DF评估数据集上测试了RawTFNet，结果表明其性能可与当前最先进模型相媲美，同时使用更少计算资源。代码与模型将公开发布。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。