Speech is Silver, Silence is Golden: What do ASVspoof-trained Models Really Learn?

TL;DR

This paper analyzes a dataset artifact in ASVspoof challenges where silence duration correlates with spoofing labels, revealing models may rely on silence duration rather than genuine speech features, affecting spoof detection reliability.

Contribution

The study uncovers the influence of silence duration artifacts in the dataset and demonstrates how models trained on silence features can achieve high accuracy, highlighting potential biases in spoof detection models.

Findings

Models trained solely on silence duration achieve up to 85% accuracy.

Silence trimming during preprocessing significantly worsens model performance.

Silence duration correlates with spoofing labels, affecting system score interpretation.

Abstract

We present our analysis of a significant data artifact in the official 2019/2021 ASVspoof Challenge Dataset. We identify an uneven distribution of silence duration in the training and test splits, which tends to correlate with the target prediction label. Bonafide instances tend to have significantly longer leading and trailing silences than spoofed instances. In this paper, we explore this phenomenon and its impact in depth. We compare several types of models trained on a) only the duration of the leading silence and b) only on the duration of leading and trailing silence. Results show that models trained on only the duration of the leading silence perform particularly well, and achieve up to 85% percent accuracy and an equal error rate (EER) of 15.1%. At the same time, we observe that trimming silence during pre-processing and then training established antispoofing models using signal-based features leads to comparatively worse performance. In that case, EER increases from 3.6% (with silence) to 15.5% (trimmed silence). Our findings suggest that previous work may, in part, have inadvertently learned thespoof/bonafide distinction by relying on the duration of silence as it appears in the official challenge dataset. We discuss the potential consequences that this has for interpreting system scores in the challenge and discuss how the ASV community may further consider this issue.