论文标题
低通滤波和带宽扩展，用于针对编解码器的强大反欺骗性对策
Low Pass Filtering and Bandwidth Extension for Robust Anti-spoofing Countermeasure Against Codec Variabilities
论文作者
论文摘要
可靠的语音反欺骗对策系统需要在各种欺骗场景中鲁棒保护自动扬声器验证（ASV）系统。但是，对策系统的性能可能会因频道效应和编解码器而降低。在本文中，我们表明，使用信号的低频子带作为输入可以减轻编解码器对对策系统引入的负面影响。为了验证这一点，将两种具有不同截止频率的低通滤波器应用于对策系统，相等的错误率（EER）相对降低了25％。此外，我们提出了一种基于深度学习的带宽扩展方法，以进一步提高检测准确性。最近的研究表明，当通过语音活动检测去除沉默部分时，对策系统的错误率急剧增加，我们的实验结果表明，在应用VAD时，在编解码条件下，滤波和带宽扩展方法也有效。
A reliable voice anti-spoofing countermeasure system needs to robustly protect automatic speaker verification (ASV) systems in various kinds of spoofing scenarios. However, the performance of countermeasure systems could be degraded by channel effects and codecs. In this paper, we show that using the low-frequency subbands of signals as input can mitigate the negative impact introduced by codecs on the countermeasure systems. To validate this, two types of low-pass filters with different cut-off frequencies are applied to countermeasure systems, and the equal error rate (EER) is reduced by up to 25% relatively. In addition, we propose a deep learning based bandwidth extension approach to further improve the detection accuracy. Recent studies show that the error rate of countermeasure systems increase dramatically when the silence part is removed by Voice Activity Detection (VAD), our experimental results show that the filtering and bandwidth extension approaches are also effective under the codec condition when VAD is applied.
