Stationary and dynamic aerodynamic assessment of vocal hyperfunction using enhanced supraglottal and subglottal inverse filtering methods

Abstract

This thesis describes the guidelines, experimental design, and initial results for stationary and dynamic aerodynamic assessments of vocal hyperfunction. This work aims to improve the understanding and clinical assessment of vocal hyperfunction by advancing current methods for the inverse filtering of both oral airflow and neck skin acceleration signals and by incorporating statistical analysis tools in this framework. New algorithms to perform inverse filtering including a frame-based approach are explored and applied in an automatic framework to estimate multiple aerodynamic measures of vocal function, and later utilized with enhanced clinical methods to differentiate vocal hyperfunction from normal vocal behavior. To achieve this goal, a clinical assessment is performed through aerodynamic, vibroacoustic, and acoustic recordings of vocal function in laboratory conditions. Various methods to improve the estimation of aerodynamic measures using different vocal gestures are explored, including sustained vowels and continuous speech. Selected inverse filtering techniques were enhanced to estimate glottal airflow in normal and pathological voices, wherein many of them constitute the most challenging conditions for inverse filtering, namely female voices during running speech. The underlying hypothesis considers that supraglottal inverse filtering methods can be enhanced under challenging conditions by limiting the signal bandwidth down to the first formant. For estimating inverse filtering quality, several error metrics that are based on the signal behavior and the contrast with glottal waveform simulations, are proposed and studied. In addition, the subglottal impedance-based inverse filtering (IBIF) scheme is explored in the context of running speech. %The assumption of time-invariance of IBIF model parameters is discussed and evaluated. The automated supraglottal inverse filtering technique was implemented in a frame-based framework to evaluate the uncertainties of the IBIF model parameters and the derived aerodynamic measures from a neck skin acceleration signal. Robust statistical analysis was used to reduce the influence of sporadic events and outliers for the objective parameter estimation. Accounting for the uncertainties of glottal airflow from the neck skin acceleration signal would allow for improving the subglottal inverse filtering, and future directions for this approach are discussed to advance the aerodynamic ambulatory monitoring of vocal function. Finally, an updated dataset of aerodynamic measures was derived with the proposed supraglottal and subglottal methods along with a robust approach to differentiate hyperfunctional patients with paired matched controls, using multivariate statistical models based on oral airflow and neck acceleration recordings.