通过波形级静音填充提升语音验证对静音段的鲁棒性
PadAug: Robust Speaker Verification with Simple Waveform-Level Silence Padding
- 在波形层面将静音段与语音段拼接，增强模型对静音的容忍度
- 在VoxCeleb数据集上使ResNet34的等错误率降低5.0%
- 适用于多种静音长度和比例，兼容现有主流模型
语音中非语音段的存在常导致语音验证性能下降。现有系统通常使用语音活动检测作为预处理步骤以去除长静音段，但语音段之间的短静音段仍会影响验证效果。为此，本文提出一种简单的波形级数据增强方法PadAug，旨在提升系统对静音段的鲁棒性。其核心思想是在模型训练时于波形层面将静音段与语音段拼接。由于方法简单，可直接应用于当前最先进的架构。实验结果表明，将PadAug应用于ResNet34，在VoxCeleb数据集上实现相对等错误率降低5.0%。此外，基于PadAug的系统对测试数据中不同长度和比例的静音段均表现稳健。
原文摘要 · Abstract (English)
The presence of non-speech segments in utterances often leads to the performance degradation of speaker verification. Existing systems usually use voice activation detection as a preprocessing step to cut off long silence segments. However, short silence segments, particularly those between speech segments, still remain a problem for speaker verification. To address this issue, in this paper, we propose a simple wave-level data augmentation method, \textit{PadAug}, which aims to enhance the system's robustness to silence segments. The core idea of \textit{PadAug} is to concatenate silence segments with speech segments at the waveform level for model training. Due to its simplicity, it can be directly applied to the current state-of-the art architectures. Experimental results demonstrate the effectiveness of the proposed \textit{PadAug}. For example, applying \textit{PadAug} to ResNet34 achieves a relative equal error rate reduction of 5.0\% on the voxceleb dataset. Moreover, the \textit{PadAug} based systems are robust to different lengths and proportions of silence segments in the test data.
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