Acoustic Screening of the “Wet voice”: Proof of Concept in an ex vivo Canine Laryngeal Model

Abstract

BackgroundCurrent protocols for bedside swallow evaluation have high rates of false negative results. Though experts are not consistently able to screen for aspiration risk by assessing vocal quality, there is emerging evidence that vocal acoustic parameters are significantly different in patients at risk of aspiration. Herein, we aimed to determine whether the presence of material on the vocal folds in an excised canine laryngeal model may have an impact on acoustic and aerodynamic measures.MethodsTwo ex vivo canine larynges were tested. Three liquids of different viscosities (1:100 diluted glycerin, pure glycerin, and honey‐thick Varibar) were placed on the vocal folds at a constant volume. Acoustic and aerodynamic measures were obtained in both adducted and abducted vocal fold configurations. Intraglottal high‐speed imaging was used to approximate the maximum divergence angle of the larynges in the studied conditions and examine its relationship to vocal efficiency (VE) and acoustic measures.ResultsIn glottic insufficiency conditions only, we found that several acoustic parameters could predict the presence of material on the vocal folds. Based on the combination of the aerodynamic and acoustic data, we found that decreased spectral energy in the higher harmonics was associated with decreased VE in the presence of material on the vocal folds and/or glottic insufficiency.ConclusionDecreased spectral energy in the higher harmonics of the voice was found to be a potential biomarker of swallowing dysfunction, as it correlates with decreased vocal efficiency due to material on the vocal folds and/or glottic insufficiency, both of which are known risk factors for aspiration.Level of EvidenceNA Laryngoscope, 133:2517–2524, 2023

Machine learning in the evaluation of voice and swallowing in the head and neck cancer patient

“…Methods for noninvasively assessing swallowing may help detect HNC patients at risk for functional disability. Several teams are investigating cough sounds and voice features as biomarkers of swallowing dysfunction using ML [70,71,72 ▪ ,73 ▪ ]. Others are studying high-resolution cervical auscultation (HRCA), which applies ML and time-series analysis to swallowing-induced acoustic and vibratory signals acquired from neck-attached sensors.…”