In text-to-speech or voice conversion based synthetic speech detection, it is a common practice that spectral information over the entire frequency band is used for feature representation. We propose a new method, referred to as subband transform, that characterizes the signals by subband. It is found that subband transform captures the artifacts in synthetic speech more effectively than full band transform. We propose equal subband transform, octave subband transform, and mel subband transform for three novel features, namely, constant-Q equal subband transform (CQ-EST), constant-Q octave subband transform (CQ-OST) and discrete Fourier mel subband transform (DF-MST). We evaluate the three features on the ASVspoof 2015, noisy ASVspoof 2015 and ASVspoof 2019 logical access corpora. The experiments show that the proposed CQ-EST feature achieves an average equal error rate of 0.056% on ASVspoof 2015 evaluation set. The study observes that the features based on subband transform outperform those based on full band transform under both clean and noisy conditions. In addition, the tandem detection cost function of CQ-OST can reach 0.188 on ASVspoof 2019 logical access evaluation set.

中文翻译：

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子带特征对于合成语音检测的意义


在基于文本到语音或语音转换的合成语音检测中，通常的做法是使用整个频带上的频谱信息来进行特征表示。我们提出了一种称为子带变换的新方法，它通过子带来表征信号。研究发现，子带变换比全带变换更能有效地捕获合成语音中的伪影。我们针对三个新特征提出了等子带变换、倍频程子带变换和梅尔子带变换，即恒定Q等子带变换（CQ-EST）、恒定Q倍频程子带变换（CQ-OST）和离散傅里叶梅尔子带变换（DF-MST）。我们评估了 ASVspoof 2015、嘈杂 ASVspoof 2015 和 ASVspoof 2019 逻辑访问语料库上的三个特征。实验表明，所提出的 CQ-EST 特征在 ASVspoof 2015 评估集上实现了 0.056% 的平均等错误率。研究发现，在干净和噪声条件下，基于子带变换的特征优于基于全带变换的特征。此外，CQ-OST的串联检测成本函数在ASVspoof 2019逻辑访问评估集上可以达到0.188。