多条件训练和语音增强方法对欺骗检测的影响

Hong Yu, A. K. Sarkar, Dennis Alexander Lehmann Thomsen, Z. Tan, Zhanyu Ma, Jun Guo
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引用次数: 37

摘要

许多研究人员已经证明了欺骗检测系统在干净的训练和测试条件下的良好性能。然而,众所周知,说话人和语音识别系统在噪声条件下的性能会显著下降。因此,研究噪声对欺骗检测系统性能的影响具有重要意义。在本文中,我们研究了一种多条件训练方法,其中欺骗检测模型使用干净和有噪声的混合数据进行训练。此外,我们研究了不同噪声类型以及语音增强方法对基于动态线性频率倒谱系数(LFCC)特征和高斯混合模型最大似然(GMM-ML)分类器的最先进欺骗检测系统的影响。在实验部分,我们考虑了Cantine、Babble和white Gaussian三种不同信噪比下的加性噪声类型,以及两种主流的语音增强方法:Wiener滤波和最小均方误差。实验结果表明,增强方法不适用于欺骗检测任务,语音增强后会降低欺骗检测精度。然而,多条件训练在降低欺骗检测的错误率方面显示出潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Effect of multi-condition training and speech enhancement methods on spoofing detection
Many researchers have demonstrated the good performance of spoofing detection systems under clean training and testing conditions. However, it is well known that the performance of speaker and speech recognition systems significantly degrades in noisy conditions. Therefore, it is of great interest to investigate the effect of noise on the performance of spoofing detection systems. In this paper, we investigate a multi-conditional training method where spoofing detection models are trained with a mix of clean and noisy data. In addition, we study the effect of different noise types as well as speech enhancement methods on a state-of-the-art spoofing detection system based on the dynamic linear frequency cepstral coefficients (LFCC) feature and a Gaussian mixture model maximum-likelihood (GMM-ML) classifier. In the experiment part we consider three additive noise types, Cantine, Babble and white Gaussian at different signal-to-noise ratios, and two mainstream speech enhancement methods, Wiener filtering and minimum mean-square error. The experimental results show that enhancement methods are not suitable for the spoofing detection task, as the spoofing detection accuracy will be reduced after speech enhancement. Multi-conditional training, however, shows potential at reducing error rates for spoofing detection.
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