The Impact of Silence on Speech Anti-Spoofing

TL;DR

This paper investigates how silence affects speech anti-spoofing systems, revealing that silence content and duration significantly influence detection accuracy, and proposes methods to improve robustness against silence-related attacks.

Contribution

The study analyzes the impact of silence on anti-spoofing models, visualizes attention distribution, and proposes masking silence to enhance robustness against unknown spoofing attacks.

Findings

Silence duration is lower in TTS spoof speech compared to bonafide speech.

Removing silence increases error rates in neural TTS spoof detection.

Masking silence improves model robustness against certain spoofing attacks.

Abstract

The current speech anti-spoofing countermeasures (CMs) show excellent performance on specific datasets. However, removing the silence of test speech through Voice Activity Detection (VAD) can severely degrade performance. In this paper, the impact of silence on speech anti-spoofing is analyzed. First, the reasons for the impact are explored, including the proportion of silence duration and the content of silence. The proportion of silence duration in spoof speech generated by text-to-speech (TTS) algorithms is lower than that in bonafide speech. And the content of silence generated by different waveform generators varies compared to bonafide speech. Then the impact of silence on model prediction is explored. Even after retraining, the spoof speech generated by neural network based end-to-end TTS algorithms suffers a significant rise in error rates when the silence is removed. To demonstrate the reasons for the impact of silence on CMs, the attention distribution of a CM is visualized through class activation mapping (CAM). Furthermore, the implementation and analysis of the experiments masking silence or non-silence demonstrates the significance of the proportion of silence duration for detecting TTS and the importance of silence content for detecting voice conversion (VC). Based on the experimental results, improving the robustness of CMs against unknown spoofing attacks by masking silence is also proposed. Finally, the attacks on anti-spoofing CMs through concatenating silence, and the mitigation of VAD and silence attack through low-pass filtering are introduced.