Source-tract decomposition (or glottal flow estimation) is one of the basic problems of speech processing. For this, several techniques have been proposed in the literature. However studies comparing different approaches are almost nonexistent. Besides, experiments have been systematically performed either on synthetic speech or on sustained vowels. In this study we compare three of the main representative state-of-the-art methods of glottal flow estimation: closed-phase inverse filtering, iterative and adaptive inverse filtering, and mixed-phase decomposition. These techniques are first submitted to an objective assessment test on synthetic speech signals. Their sensitivity to various factors affecting the estimation quality, as well as their robustness to noise are studied. In a second experiment, their ability to label voice quality (tensed, modal, soft) is studied on a large corpus of real connected speech. It is shown that changes of voice quality are reflected by significant modifications in glottal feature distributions. Techniques based on the mixed-phase decomposition and on a closed-phase inverse filtering process turn out to give the best results on both clean synthetic and real speech signals. On the other hand, iterative and adaptive inverse filtering is recommended in noisy environments for its high robustness.

中文翻译：

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声门源估计技术的比较研究

源道分解（或声门流估计）是语音处理的基本问题之一。为此，文献中提出了几种技术。然而，比较不同方法的研究几乎不存在。此外，已经系统地对合成语音或持续元音进行了实验。在这项研究中，我们比较了三种主要的代表性最先进的声门流量估计方法：闭相逆滤波、迭代和自适应逆滤波以及混合相位分解。这些技术首先提交给对合成语音信号的客观评估测试。研究了它们对影响估计质量的各种因素的敏感性，以及它们对噪声的鲁棒性。在第二个实验中，他们标记语音质量的能力（时态，modal, soft) 在一个大型的真实连接语音语料库上进行研究。结果表明，声门特征分布的显着改变反映了语音质量的变化。事实证明，基于混合相位分解和闭相位逆滤波过程的技术对干净的合成语音信号和真实语音信号都能提供最佳结果。另一方面，在嘈杂的环境中推荐迭代和自适应逆滤波，因为它具有高鲁棒性。