An Empirical Study on Channel Effects for Synthetic Voice Spoofing Countermeasure Systems

TL;DR

This study investigates how channel variability affects synthetic voice spoofing countermeasure systems and proposes strategies to improve their robustness across different acoustic conditions.

Contribution

It identifies channel mismatch as a key factor in performance degradation and introduces robust training methods to mitigate this issue.

Findings

Channel mismatch significantly degrades CM system performance.

Channel-robust strategies improve cross-dataset performance.

Data augmentation and adversarial learning enhance robustness.

Abstract

Spoofing countermeasure (CM) systems are critical in speaker verification; they aim to discern spoofing attacks from bona fide speech trials. In practice, however, acoustic condition variability in speech utterances may significantly degrade the performance of CM systems. In this paper, we conduct a cross-dataset study on several state-of-the-art CM systems and observe significant performance degradation compared with their single-dataset performance. Observing differences of average magnitude spectra of bona fide utterances across the datasets, we hypothesize that channel mismatch among these datasets is one important reason. We then verify it by demonstrating a similar degradation of CM systems trained on original but evaluated on channel-shifted data. Finally, we propose several channel robust strategies (data augmentation, multi-task learning, adversarial learning) for CM systems, and observe a significant performance improvement on cross-dataset experiments.