Audio Transformer for Synthetic Speech Detection via Benford's Law Distribution Analysis

被引：2

作者：

Ashoka, Anitha Bhat Talagini ^{[1
]}

Cuccovillo, Luca ^{[1
]}

Aichroth, Patrick ^{[1
]}

机构：

[1] Fraunhofer IDMT, Ilmenau, Germany

来源：

PROCEEDINGS OF THE 3RD ACM INTERNATIONAL WORKSHOP ON MULTIMEDIA AI AGAINST DISINFORMATION, MAD 2024 | 2024年

关键词：

synthetic speech detection; audio deepfakes; audio transformer; Benford's law; 1ST DIGIT;

D O I：

10.1145/3643491.3660288

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper introduces a novel approach that enhances synthetic speech detection by applying Benford's law analysis to the encoder embeddings. By leveraging Benford's Law as supplementary information alongside the existing embeddings, the model gains a detailed understanding of both content-related features and numerical distribution patterns. Our approach demonstrates superior performance on the ASVspoof 2019 LA dataset, achieving an AUC score of 0.921, while providing enhanced interpretability.

引用

页码：23 / 29

页数：7

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