Studying the Laryngeal Mechanisms Underlying Dysphonia in Connected Speech
Studying the Laryngeal Mechanisms Underlying Dysphonia in Connected Speech
批准号:
10608001
负责人:
Maryam Naghibolhosseini
金额:
$13.78万
依托单位国家:
美国
项目类别:
财政年份:
2019
资助国家:
美国
项目状态:
未结题
起止时间:
2019-04-01 至 2025-03-31
关键词:
AcousticsAddressAgeAreaAuditoryBehaviorBig DataBiomechanicsCategoriesCharacteristicsClinicalCommunicationCouplingDataData SetDevelopmentDiagnosisDysphoniaEndoscopesEvaluationFiber OpticsFunctional disorderGoalsHealthHumanImageKnowledgeLarynxMachine LearningMeasurementMeasuresMedicineMethodologyMethodsMiningNational Institute on Deafness and Other Communication DisordersOperative Surgical ProceduresOutcomeOutcomes ResearchParalysedPatientsPersonsPhoneticsPhysicsPhysiologicalPreventionProductionProtocols documentationResearchSeriesSeveritiesSourceSpastic DysphoniasSpeechSpeedStatistical Data InterpretationStatistical ModelsStrategic PlanningSystemTechniquesTestingTherapeuticTimeTremorVisualVoiceVoice DisordersVoice DisturbancesVoice Qualityclinical applicationclinical developmentclinical practiceclinically relevantcohortflexibilityimage processingimprovedinnovationkinematicsmachine learning modelnovel therapeutic interventionoptical fibersextemporal measurementtime usetoolvibrationvocal cordvocalization
中文摘要
点击翻译按钮获取中文摘要
英文摘要
Project Summary/Abstract
This proposal aims to employ the recent advancement of coupling fiberoptic endoscopes with high-speed
videoendoscopy (HSV) systems to obtain HSV recordings during connected speech. The goal is to study vocal
mechanisms underlying dysphonia in patients with neurogenic voice disorders. The long-term goal of this line of
research is to create clinically applicable quantitative methods for functional measurement of vocal fold vibration
in connected speech using innovative laryngeal imaging, an approach that could advance clinical voice
assessment and treatment practice. In Aim 1, HSV-based measures of vocal fold kinematics will be developed
and the influence of these measures on voice audio-perceptual qualities in the patients will be determined. Image
processing techniques will be developed to extract such measures from the HSV data in connected speech. The
extracted measures will be given as inputs to the statistical models to determine the source of the differences
between the normal controls and the patients for different speech phonetic contexts and words. This aim provides
an unbiased HSV-based method to predict voice quality. Developing such HSV-based methodology for functional
laryngeal examination in connected speech can enhance clinical voice assessment. In addition, better
understanding the influence of phonetic context would lead to optimizing the protocols for functional voice
assessment through laryngeal imaging in connected speech. In Aim 2, machine learning approaches will be
employed to discover hidden physics and unknown laryngeal mechanisms of voice production in the dysphonic
patients. The findings of this project will help make necessary adjustments in biomechanical or physiological
characteristics of vocal folds to enhance voice quality in patients with neurogenic voice disorders. Therefore, the
outcome of this research will aid clinicians in properly selecting, and developing new treatment strategies
(therapeutic, medicinal, or surgical), which are based on the gained knowledge of laryngeal mechanisms of
dysphonia. The proposed research is in harmony with multiple priority areas of the NIDCD, described in the
2017-2021 Strategic Plan. Both aims support Priority 3 (improve methods of diagnosis, treatment, and
prevention) through developing objective HSV-based measures and predicting the voice quality. Comparing
laryngeal mechanisms in normal and disordered voices addresses Priority 1 (deepen our understanding of the
normal function of the systems of human communication). Both aims propose to study laryngeal mechanisms in
patients with neurogenic and functional voice disorders, which addresses Priority 2 (increase our knowledge
about conditions that alter or diminish communication and health).
期刊论文(3)
专著(0)
科研奖励(0)
会议论文
DOI:
10.3390/acoustics5010005
发表时间:
2023-03
期刊:
Acoustics (Basel, Switzerland)
影响因子:
--
作者:
[]
通讯作者:
DOI:
10.1016/j.jvoice.2022.01.028
发表时间:
2024-07
期刊:
JOURNAL OF VOICE
影响因子:
2.2
作者:
[Yousef, Ahmed M., Deliyski, Dimitar D., Zacharias, Stephanie R. C., Naghibolhosseini, Maryam]
通讯作者:
Naghibolhosseini, Maryam
Bayesian Data-Driven Subject-Specific Modeling of Voice Production
-
批准号:10360108
-
项目类别:
-
资助金额:$18.8万
-
财政年份:2022
-
负责人:Maryam Naghibolhosseini
-
依托单位:
Bayesian Data-Driven Subject-Specific Modeling of Voice Production
-
批准号:10904247
-
项目类别:
-
资助金额:$6.89万
-
财政年份:2022
-
负责人:Maryam Naghibolhosseini
-
依托单位:
Bayesian Data-Driven Subject-Specific Modeling of Voice Production
-
批准号:10609493
-
项目类别:
-
资助金额:$18.77万
-
财政年份:2022
-
负责人:Maryam Naghibolhosseini
-
依托单位:
Studying the Laryngeal Mechanisms Underlying Dysphonia in Connected Speech
-
批准号:9901502
-
项目类别:
-
资助金额:$13.78万
-
财政年份:2019
-
负责人:Maryam Naghibolhosseini
-
依托单位:
Studying the Laryngeal Mechanisms Underlying Dysphonia in Connected Speech
-
批准号:10378024
-
项目类别:
-
资助金额:$13.78万
-
财政年份:2019
-
负责人:Maryam Naghibolhosseini
-
依托单位:
海外基金