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...

Autores
Ibarra, Emiro J.; Parra, Jesús A.; Alzamendi, Gabriel Alejandro; Cortés, Juan P.; Espinoza, Víctor M.; Mehta, Daryush D.; Hillman, Robert E.; Zañartu, Matías
Año de publicación
2021
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
The ambulatory assessment of vocal function can be significantly enhanced by having access to physiologically based features that describe underlying pathophysiological mechanisms in individuals with voice disorders. This type of enhancement can improve methods for the prevention, diagnosis, and treatment of behaviorally based voice disorders. Unfortunately, the direct measurement of important vocal features such as subglottal pressure, vocal fold collision pressure, and laryngeal muscle activation is impractical in laboratory and ambulatory settings. In this study, we introduce a method to estimate these features during phonation from a neck-surface vibration signal through a framework that integrates a physiologically relevant model of voice production and machine learning tools. The signal from a neck-surface accelerometer is first processed using subglottal impedance-based inverse filtering to yield an estimate of the unsteady glottal airflow. Seven aerodynamic and acoustic features are extracted from the neck surface accelerometer and an optional microphone signal. A neural network architecture is selected to provide a mapping between the seven input features and subglottal pressure, vocal fold collision pressure, and cricothyroid and thyroarytenoid muscle activation. This non-linear mapping is trained solely with 13,000 Monte Carlo simulations of a voice production model that utilizes a symmetric triangular body-cover model of the vocal folds. The performance of the method was compared against laboratory data from synchronous recordings of oral airflow, intraoral pressure, microphone, and neck-surface vibration in 79 vocally healthy female participants uttering consecutive /pæ/ syllable strings at comfortable, loud, and soft levels. The mean absolute error and root-mean-square error for estimating the mean subglottal pressure were 191 Pa (1.95 cm H2O) and 243 Pa (2.48 cm H2O), respectively, which are comparable with previous studies but with the key advantage of not requiring subject-specific training and yielding more output measures. The validation of vocal fold collision pressure and laryngeal muscle activation was performed with synthetic values as reference. These initial results provide valuable insight for further vocal fold model refinement and constitute a proof of concept that the proposed machine learning method is a feasible option for providing physiologically relevant measures for laboratory and ambulatory assessment of vocal function.
Fil: Ibarra, Emiro J.. Universidad Tecnica Federico Santa Maria.; Chile
Fil: Parra, Jesús A.. Universidad Tecnica Federico Santa Maria.; Chile
Fil: Alzamendi, Gabriel Alejandro. Universidad Nacional de Entre Ríos. Instituto de Investigación y Desarrollo en Bioingeniería y Bioinformática - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación y Desarrollo en Bioingeniería y Bioinformática; Argentina
Fil: Cortés, Juan P.. Universidad Tecnica Federico Santa Maria.; Chile
Fil: Espinoza, Víctor M.. Universidad de Chile; Chile
Fil: Mehta, Daryush D.. Center For Laryngeal Surgery And Voice Rehabilitation; Estados Unidos
Fil: Hillman, Robert E.. Center For Laryngeal Surgery And Voice Rehabilitation; Estados Unidos
Fil: Zañartu, Matías. Universidad Tecnica Federico Santa Maria.; Chile
Materia
AMBULATORY MONITORING
CLINICAL VOICE ASSESSMENT
NECK-SURFACE ACCELEROMETER
NEURAL NETWORKS
SUBGLOTTAL PRESSURE ESTIMATION
VOICE PRODUCTION MODEL
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/173425
Acceder
id CONICETDig_e610bf8c770ac41372a7c8e42955d7ae
oai_identifier_str oai:ri.conicet.gov.ar:11336/173425
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling 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 ModelIbarra, Emiro J.Parra, Jesús A.Alzamendi, Gabriel AlejandroCortés, Juan P.Espinoza, Víctor M.Mehta, Daryush D.Hillman, Robert E.Zañartu, MatíasAMBULATORY MONITORINGCLINICAL VOICE ASSESSMENTNECK-SURFACE ACCELEROMETERNEURAL NETWORKSSUBGLOTTAL PRESSURE ESTIMATIONVOICE PRODUCTION MODELhttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1The ambulatory assessment of vocal function can be significantly enhanced by having access to physiologically based features that describe underlying pathophysiological mechanisms in individuals with voice disorders. This type of enhancement can improve methods for the prevention, diagnosis, and treatment of behaviorally based voice disorders. Unfortunately, the direct measurement of important vocal features such as subglottal pressure, vocal fold collision pressure, and laryngeal muscle activation is impractical in laboratory and ambulatory settings. In this study, we introduce a method to estimate these features during phonation from a neck-surface vibration signal through a framework that integrates a physiologically relevant model of voice production and machine learning tools. The signal from a neck-surface accelerometer is first processed using subglottal impedance-based inverse filtering to yield an estimate of the unsteady glottal airflow. Seven aerodynamic and acoustic features are extracted from the neck surface accelerometer and an optional microphone signal. A neural network architecture is selected to provide a mapping between the seven input features and subglottal pressure, vocal fold collision pressure, and cricothyroid and thyroarytenoid muscle activation. This non-linear mapping is trained solely with 13,000 Monte Carlo simulations of a voice production model that utilizes a symmetric triangular body-cover model of the vocal folds. The performance of the method was compared against laboratory data from synchronous recordings of oral airflow, intraoral pressure, microphone, and neck-surface vibration in 79 vocally healthy female participants uttering consecutive /pæ/ syllable strings at comfortable, loud, and soft levels. The mean absolute error and root-mean-square error for estimating the mean subglottal pressure were 191 Pa (1.95 cm H2O) and 243 Pa (2.48 cm H2O), respectively, which are comparable with previous studies but with the key advantage of not requiring subject-specific training and yielding more output measures. The validation of vocal fold collision pressure and laryngeal muscle activation was performed with synthetic values as reference. These initial results provide valuable insight for further vocal fold model refinement and constitute a proof of concept that the proposed machine learning method is a feasible option for providing physiologically relevant measures for laboratory and ambulatory assessment of vocal function.Fil: Ibarra, Emiro J.. Universidad Tecnica Federico Santa Maria.; ChileFil: Parra, Jesús A.. Universidad Tecnica Federico Santa Maria.; ChileFil: Alzamendi, Gabriel Alejandro. Universidad Nacional de Entre Ríos. Instituto de Investigación y Desarrollo en Bioingeniería y Bioinformática - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación y Desarrollo en Bioingeniería y Bioinformática; ArgentinaFil: Cortés, Juan P.. Universidad Tecnica Federico Santa Maria.; ChileFil: Espinoza, Víctor M.. Universidad de Chile; ChileFil: Mehta, Daryush D.. Center For Laryngeal Surgery And Voice Rehabilitation; Estados UnidosFil: Hillman, Robert E.. Center For Laryngeal Surgery And Voice Rehabilitation; Estados UnidosFil: Zañartu, Matías. Universidad Tecnica Federico Santa Maria.; ChileFrontiers Media2021-09info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/173425Ibarra, Emiro J.; Parra, Jesús A.; Alzamendi, Gabriel Alejandro; Cortés, Juan P.; Espinoza, Víctor M.; et al.; 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; Frontiers Media; Frontiers in Physiology; 12; 9-2021; 1-131664-042XCONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.frontiersin.org/articles/10.3389/fphys.2021.732244/fullinfo:eu-repo/semantics/altIdentifier/doi/10.3389/fphys.2021.732244info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:04:44Zoai:ri.conicet.gov.ar:11336/173425instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 10:04:44.377CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv 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
title 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
spellingShingle 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
Ibarra, Emiro J.
AMBULATORY MONITORING
CLINICAL VOICE ASSESSMENT
NECK-SURFACE ACCELEROMETER
NEURAL NETWORKS
SUBGLOTTAL PRESSURE ESTIMATION
VOICE PRODUCTION MODEL
title_short 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
title_full 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
title_fullStr 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
title_full_unstemmed 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
title_sort 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
dc.creator.none.fl_str_mv Ibarra, Emiro J.
Parra, Jesús A.
Alzamendi, Gabriel Alejandro
Cortés, Juan P.
Espinoza, Víctor M.
Mehta, Daryush D.
Hillman, Robert E.
Zañartu, Matías
author Ibarra, Emiro J.
author_facet Ibarra, Emiro J.
Parra, Jesús A.
Alzamendi, Gabriel Alejandro
Cortés, Juan P.
Espinoza, Víctor M.
Mehta, Daryush D.
Hillman, Robert E.
Zañartu, Matías
author_role author
author2 Parra, Jesús A.
Alzamendi, Gabriel Alejandro
Cortés, Juan P.
Espinoza, Víctor M.
Mehta, Daryush D.
Hillman, Robert E.
Zañartu, Matías
author2_role author
author
author
author
author
author
author
dc.subject.none.fl_str_mv AMBULATORY MONITORING
CLINICAL VOICE ASSESSMENT
NECK-SURFACE ACCELEROMETER
NEURAL NETWORKS
SUBGLOTTAL PRESSURE ESTIMATION
VOICE PRODUCTION MODEL
topic AMBULATORY MONITORING
CLINICAL VOICE ASSESSMENT
NECK-SURFACE ACCELEROMETER
NEURAL NETWORKS
SUBGLOTTAL PRESSURE ESTIMATION
VOICE PRODUCTION MODEL
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv The ambulatory assessment of vocal function can be significantly enhanced by having access to physiologically based features that describe underlying pathophysiological mechanisms in individuals with voice disorders. This type of enhancement can improve methods for the prevention, diagnosis, and treatment of behaviorally based voice disorders. Unfortunately, the direct measurement of important vocal features such as subglottal pressure, vocal fold collision pressure, and laryngeal muscle activation is impractical in laboratory and ambulatory settings. In this study, we introduce a method to estimate these features during phonation from a neck-surface vibration signal through a framework that integrates a physiologically relevant model of voice production and machine learning tools. The signal from a neck-surface accelerometer is first processed using subglottal impedance-based inverse filtering to yield an estimate of the unsteady glottal airflow. Seven aerodynamic and acoustic features are extracted from the neck surface accelerometer and an optional microphone signal. A neural network architecture is selected to provide a mapping between the seven input features and subglottal pressure, vocal fold collision pressure, and cricothyroid and thyroarytenoid muscle activation. This non-linear mapping is trained solely with 13,000 Monte Carlo simulations of a voice production model that utilizes a symmetric triangular body-cover model of the vocal folds. The performance of the method was compared against laboratory data from synchronous recordings of oral airflow, intraoral pressure, microphone, and neck-surface vibration in 79 vocally healthy female participants uttering consecutive /pæ/ syllable strings at comfortable, loud, and soft levels. The mean absolute error and root-mean-square error for estimating the mean subglottal pressure were 191 Pa (1.95 cm H2O) and 243 Pa (2.48 cm H2O), respectively, which are comparable with previous studies but with the key advantage of not requiring subject-specific training and yielding more output measures. The validation of vocal fold collision pressure and laryngeal muscle activation was performed with synthetic values as reference. These initial results provide valuable insight for further vocal fold model refinement and constitute a proof of concept that the proposed machine learning method is a feasible option for providing physiologically relevant measures for laboratory and ambulatory assessment of vocal function.
Fil: Ibarra, Emiro J.. Universidad Tecnica Federico Santa Maria.; Chile
Fil: Parra, Jesús A.. Universidad Tecnica Federico Santa Maria.; Chile
Fil: Alzamendi, Gabriel Alejandro. Universidad Nacional de Entre Ríos. Instituto de Investigación y Desarrollo en Bioingeniería y Bioinformática - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación y Desarrollo en Bioingeniería y Bioinformática; Argentina
Fil: Cortés, Juan P.. Universidad Tecnica Federico Santa Maria.; Chile
Fil: Espinoza, Víctor M.. Universidad de Chile; Chile
Fil: Mehta, Daryush D.. Center For Laryngeal Surgery And Voice Rehabilitation; Estados Unidos
Fil: Hillman, Robert E.. Center For Laryngeal Surgery And Voice Rehabilitation; Estados Unidos
Fil: Zañartu, Matías. Universidad Tecnica Federico Santa Maria.; Chile
description The ambulatory assessment of vocal function can be significantly enhanced by having access to physiologically based features that describe underlying pathophysiological mechanisms in individuals with voice disorders. This type of enhancement can improve methods for the prevention, diagnosis, and treatment of behaviorally based voice disorders. Unfortunately, the direct measurement of important vocal features such as subglottal pressure, vocal fold collision pressure, and laryngeal muscle activation is impractical in laboratory and ambulatory settings. In this study, we introduce a method to estimate these features during phonation from a neck-surface vibration signal through a framework that integrates a physiologically relevant model of voice production and machine learning tools. The signal from a neck-surface accelerometer is first processed using subglottal impedance-based inverse filtering to yield an estimate of the unsteady glottal airflow. Seven aerodynamic and acoustic features are extracted from the neck surface accelerometer and an optional microphone signal. A neural network architecture is selected to provide a mapping between the seven input features and subglottal pressure, vocal fold collision pressure, and cricothyroid and thyroarytenoid muscle activation. This non-linear mapping is trained solely with 13,000 Monte Carlo simulations of a voice production model that utilizes a symmetric triangular body-cover model of the vocal folds. The performance of the method was compared against laboratory data from synchronous recordings of oral airflow, intraoral pressure, microphone, and neck-surface vibration in 79 vocally healthy female participants uttering consecutive /pæ/ syllable strings at comfortable, loud, and soft levels. The mean absolute error and root-mean-square error for estimating the mean subglottal pressure were 191 Pa (1.95 cm H2O) and 243 Pa (2.48 cm H2O), respectively, which are comparable with previous studies but with the key advantage of not requiring subject-specific training and yielding more output measures. The validation of vocal fold collision pressure and laryngeal muscle activation was performed with synthetic values as reference. These initial results provide valuable insight for further vocal fold model refinement and constitute a proof of concept that the proposed machine learning method is a feasible option for providing physiologically relevant measures for laboratory and ambulatory assessment of vocal function.
publishDate 2021
dc.date.none.fl_str_mv 2021-09
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/173425
Ibarra, Emiro J.; Parra, Jesús A.; Alzamendi, Gabriel Alejandro; Cortés, Juan P.; Espinoza, Víctor M.; et al.; 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; Frontiers Media; Frontiers in Physiology; 12; 9-2021; 1-13
1664-042X
CONICET Digital
CONICET
url http://hdl.handle.net/11336/173425
identifier_str_mv Ibarra, Emiro J.; Parra, Jesús A.; Alzamendi, Gabriel Alejandro; Cortés, Juan P.; Espinoza, Víctor M.; et al.; 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; Frontiers Media; Frontiers in Physiology; 12; 9-2021; 1-13
1664-042X
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://www.frontiersin.org/articles/10.3389/fphys.2021.732244/full
info:eu-repo/semantics/altIdentifier/doi/10.3389/fphys.2021.732244
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Frontiers Media
publisher.none.fl_str_mv Frontiers Media
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1844613876341014528
score 13.070432

Publicaciones similares