LAUSR

The aim of this work is to determine the uncertainty of non-invasive glottal aerodynamic measures that are obtained using subglottal impedance-based inverse filtering (IBIF) of the signal from a neck-placed accelerometer during continuous speech. Currently, we are studying the vocal behavior of individuals with typical voices and voice disorders by analyzing weeklong recordings using a smartphone-based ambulatory voice monitor. We extend on previously reported analyses of sustained vowel production using subglottal IBIF and move toward continuous speech applications where IBIF parameters are estimated in a frame-based approach. Selected voiced frames of both oral-airflow (baseline) and acceleration signal from the Rainbow Passage are used to build a probabilistic model of IBIF parameters to run multiple random realizations of the inverse-filtered neck-surface acceleration signal. Confidence intervals are estimated for both the glottal waveform and derived features. The probabilistic model is tested using ...