Ambulatory assessment of phonotraumatic vocal hyperfunction using glottal airflow measures estimated from neck-surface acceleration

Abstract

Phonotraumatic vocal hyperfunction (PVH) is associated with chronic misuse and/or abuse of voice that can result in lesions such as vocal fold nodules. The clinical aerodynamic assessment of vocal function has been recently shown to differentiate between patients with PVH and healthy controls to provide meaningful insight into pathophysiological mechanisms associated with these disorders. However, all current clinical assessment of PVH is incomplete because of its inability to objectively identify the type and extent of detrimental phonatory function that is associated with PVH during daily voice use. The current study sought to address this issue by incorporating, for the first time in a comprehensive ambulatory assessment, glottal airflow parameters estimated from a neck-mounted accelerometer and recorded to a smartphone-based voice monitor. We tested this approach on 48 patients with vocal fold nodules and 48 matched healthy-control subjects who each wore the voice monitor for a week. Seven glottal airflow features were estimated every 50 ms using an impedance-based inverse filtering scheme, and seven high-order summary statistics of each feature were computed every 5 minutes over voiced segments. Based on a univariate hypothesis testing, eight glottal airflow summary statistics were found to be statistically different between patient and healthy-control groups. L1-regularized logistic regression for a supervised classification task yielded a mean (standard deviation) area under the ROC curve of 0.82 (0.25) and an accuracy of 0.83 (0.14). These results outperform the state-of-the-art classification for the same classification task and provide a new avenue to improve the assessment and treatment of hyperfunctional voice disorders.

Impact of Nonmodal Phonation on Estimates of Subglottal Pressure From Neck-Surface Acceleration in Healthy Speakers

“…equation would help to minimize the error in predicting Ps. Other potential metrics sensitive to voice mode include open quotient (Yokonishi et al, 2016), which could be obtained from ACC-based glottal airflow estimates derived using, for example, impedance-based inverse filtering (Cortés et al, 2018;Zañartu et al, 2013). Incorporation of additional ACC-based measures may be applied to help delineate different voice modes and characterize pathological glottal conditions that are associated with varying degrees of glottal closure, vocal fold stiffness/tension, and adduction forces.…”

Acoustic Identification of the Voicing Boundary during Intervocalic Offsets and Onsets Based on Vocal Fold Vibratory Measures

“…In doing so, the aRFF-APH algorithm could be modified to include pitch strength-tuned algorithm parameters to account for variations in voice sample characteristics. The aRFF-APH algorithms should also be expanded to neck-surface accelerometer signals, as there has been a growing interest in using the neck-surface vibrations generated during speech for ecological momentary assessment and ambulatory voice monitoring (e.g., [27,[51][52][53][54][55][56][57][58][59][60]). By capturing daily vocal behavior through a neck-surface accelerometer, vocal behaviors associated with excessive or imbalanced laryngeal muscle forces could be identified and monitored via RFF.…”

Estimation of Subglottal Pressure, Vocal Fold Collision Pressure, and Intrinsic Laryngeal Muscle Activation From Neck-Surface Vibration Using a Neural Network Framework and a Voice Production Model

“…Numerous features have been extracted from the ambulatory recording of the ACC signal, including phonation duration, sound pressure level (SPL), fundamental frequency (f o ) (Ghassemi et al, 2014), vocal vibration-dose measures (Titze et al, 2003;Titze and Hunter, 2015), spectral and cepstral measures (Mehta et al, 2015(Mehta et al, , 2019, and aerodynamic measures (Llico et al, 2015;Cortés et al, 2018). These measures have been used to differentiate the daily voice use of patients with vocal hyperfunction from matched controls (Ghassemi et al, 2014;Cortés et al, 2018;Van Stan et al, 2021) and to track changes related to surgical and voice therapy treatment of hyperfunctional voice disorders (Van Stan et al, 2017b, 2020. Current classification accuracy using these parameters is in the range of 0.7-0.85.…”

“…Thus, we propose a method to obtain a non-linear optimal mapping between ACC features and subglottal pressure, vocal fold collision pressure, and laryngeal muscle activation. We propose using the impedance based inverse filtering (IBIF) algorithm (Zañartu et al, 2013;Cortés et al, 2018), which yields an unsteady glottal airflow signal from the ACC signal, to provide aerodynamic features that are used as inputs to the non-linear mapping. At the same time, we propose using a neural network (NN) regression architecture trained from a physiologically relevant muscle-controlled voice synthesizer with a triangular body-cover vocal fold model (Alzamendi et al, 2019(Alzamendi et al, , 2021) that takes the aerodynamic features as input and provides subglottal pressure, collision pressure, and laryngeal muscle activation as output.…”

“…Ambulatory voice monitoring using a neck-placed accelerometer (ACC) provides the capability to quantitatively assess daily vocal function and has also been shown to have the potential to assist in modifying vocal behaviors via ambulatory biofeedback (Popolo et al, 2005 ; Hillman and Mehta, 2011 ; Mehta et al, 2012 ; Andreassen et al, 2017 ; Van Stan et al, 2017a ). Numerous features have been extracted from the ambulatory recording of the ACC signal, including phonation duration, sound pressure level (SPL), fundamental frequency ( f o ) (Ghassemi et al, 2014 ), vocal vibration-dose measures (Titze et al, 2003 ; Titze and Hunter, 2015 ), spectral and cepstral measures (Mehta et al, 2015 , 2019 ), and aerodynamic measures (Llico et al, 2015 ; Cortés et al, 2018 ). These measures have been used to differentiate the daily voice use of patients with vocal hyperfunction from matched controls (Ghassemi et al, 2014 ; Cortés et al, 2018 ; Van Stan et al, 2021 ) and to track changes related to surgical and voice therapy treatment of hyperfunctional voice disorders (Van Stan et al, 2017b , 2020 ).…”