A Comparative Study of Glottal Source Estimation Techniques

TL;DR

This study compares three leading glottal flow estimation techniques, evaluating their accuracy, robustness, and ability to distinguish voice qualities on synthetic and real speech data.

Contribution

It provides a comprehensive comparison of state-of-the-art methods for glottal flow estimation, including their performance and robustness in different conditions.

Findings

Mixed-phase decomposition and closed-phase inverse filtering perform best on clean speech.

Iterative and adaptive inverse filtering are more robust in noisy environments.

Changes in voice quality significantly affect glottal feature distributions.

Abstract

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.