The use of non-invasive skin accelerometers placed over the extrathoracic trachea has been proposed in the literature for measuring vocal function. Glottal airflow is estimated using inverse filtering or Bayesian techniques based on a subglottal impedance-based model when utilizing these sensors. However, deviations in glottal airflow estimates can arise due to sensor positioning and model mismatch, and addressing them requires a significant computational load. In this paper, we utilize system identification techniques to obtain a low order state-space representation of the subglottal impedance-based model. We then employ the resulting low order model in a Kalman smoother to estimate the glottal airflow. Our proposed approach reduces the model order by 94% and requires only 1.5% of the computing time compared to previous Bayesian methods in the literature, while achieving slightly better accuracy when correcting for glottal airflow deviations. Additionally, our Kalman smoother approach provides a measure of uncertainty in the airflow estimate, which is valuable when measurements are taken under different conditions. With its comparable accuracy in signal estimation and reduced computational load, the proposed approach has the potential for real-time estimation of glottal airflow and its associated uncertainty in wearable voice ambulatory monitors using neck-surface acceleration.
Keywords: Kalman Smoothing; System Identification; Vocal Folds; Vocal Hyperfunction.