Spectral jitter modeling and estimation

This paper suggests a new method for short-time jitter estimation based on a mathematical model that describes the coupling of two periodical phenomena. Specifically, jitter is modeled as the movement of one of the two periodic phenomena with respect to the other. The proposed method measures this movement indirectly by taking into account the spectral properties of the suggested model. Experiments with synthetic jitter signals showed that the suggested method produces accurate local estimates of jitter. Further evaluation was conducted on actual normal and pathological voice signals using two databases and jitter estimations from the Multi-Dimension Voice Program (MDVP) and the Praat system. Compared with the corresponding parameters from these two systems, the proposed method outperformed both in normal vs. pathological voice discrimination accuracy by at least 4%. Furthermore, it was shown that these methods actually rely on low-pass information, while the proposed method takes into account the full spectrum. The study of the short-time statistics of the jitter measurements provided by the suggested method indicates that there is a higher correlation with voice pathology compared to that obtained by the other two systems.