Previous works in synthetic speech detection have focused on features based on magnitude or phase spectrum. In this study, to extract useful discriminative information for synthetic speech detection, the authors propose a feature based on magnitude–phase spectrum (MPS), combining magnitude- and phase-spectra information. The proposed feature is termed as constant-Q magnitude–phase coefficient (CMPC), which is obtained by combining constant-Q transform (CQT), MPS, uniform resampling, and discrete cosine transform. The CQT used in this study is a long-term window transform, which can provide the basis for CMPC to capture important artefacts of synthetic speech. Such artefacts are obtained using a unit selection algorithm, which have difficulties when based on the short-term window transform. Uniform resampling aims to convert MPS from the octave domain into the linear domain. The discrete cosine transform is used when extracting principal components to remove correlations among the feature dimensions. The experimental results on AVspoof and ASVspoof 2015 corpora show that CMPC performs better than some commonly used features based on magnitude or phase spectrum alone. Their system based on CMPC outperforms many known systems.

中文翻译：

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用于合成语音检测的恒定Q幅度-相位系数提取

合成语音检测的先前工作集中在基于幅度或相位谱的特征上。在这项研究中，为了提取有用的判别信息以进行合成语音检测，作者提出了一种基于幅度相位频谱（MPS）的功能，结合了幅度频谱信息和相位频谱信息。提出的功能称为恒定Q幅相系数（CMPC），它是通过将恒定Q变换（CQT），MPS，均匀重采样和离散余弦变换相结合而获得的。在这项研究中使用的CQT是一个长期的窗口变换，它可以为CMPC捕获合成语音的重要伪像提供基础。这些伪像是使用单位选择算法获得的，当基于短期窗口变换时，会遇到困难。均匀重采样旨在将MPS从八度域转换为线性域。提取主成分以消除特征维之间的相关性时，将使用离散余弦变换。在AVspoof和ASVspoof 2015语料库上的实验结果表明，CMPC的性能优于仅基于幅度或相位谱的一些常用功能。他们基于CMPC的系统胜过许多已知的系统。