Results 11 to 20 of about 4,036,217 (345)

Frameworks, Terminology and Definitions Used for the Classification of Voice Disorders: A Scoping Review

open access: yesJournal of Voice, 2022
Background: A challenge for clinicians and researchers in laryngology is a lack of international consensus for an agreed framework to classify homogenous groups of voice disorders. Consistency in terminology and agreement in how conditions are classified
Weir, KA   +3 more
core   +2 more sources

Prevalence of Voice Disorders in the General Population, Based on the Stockholm Public Health Cohort

open access: yesJournal of Voice, 2019
Objective: To investigate the prevalence of voice disorders in the general population. Study design: Analysis of data from the Stockholm Public Health Cohort.
Rydell, Roland,   +5 more
core   +3 more sources

Measuring Perceived Voice Disorders and Quality of Life among Female University Teaching Faculty. [PDF]

open access: yesJ Pers Med, 2023
Background: Occupations that require heavy vocal use can place the person at risk of voice disorders (VDs). Heavy demands on the voice, especially for a long time or with loud back-ground noise, can lead to vocal abuse or misuse.
Al Awaji NN   +7 more
europepmc   +2 more sources

Continuous Speech for Improved Learning Pathological Voice Disorders. [PDF]

open access: yesIEEE Open J Eng Med Biol, 2022
Goal: Numerous studies had successfully differentiated normal and abnormal voice samples. Nevertheless, further classification had rarely been attempted. This study proposes a novel approach, using continuous Mandarin speech instead of a single vowel, to
Wang SS   +4 more
europepmc   +3 more sources

Using SincNet for Learning Pathological Voice Disorders. [PDF]

open access: yesSensors (Basel), 2022
Deep learning techniques such as convolutional neural networks (CNN) have been successfully applied to identify pathological voices. However, the major disadvantage of using these advanced models is the lack of interpretability in explaining the ...
Hung CH, Wang SS, Wang CT, Fang SH.
europepmc   +2 more sources

The Effectiveness of Supervised Machine Learning in Screening and Diagnosing Voice Disorders: Systematic Review and Meta-analysis. [PDF]

open access: yesJ Med Internet Res, 2022
Background When investigating voice disorders a series of processes are used when including voice screening and diagnosis. Both methods have limited standardized tests, which are affected by the clinician’s experience and subjective judgment.
Al-Hussain G   +4 more
europepmc   +2 more sources

Prevalence of Voice Disorders in the United States: A National Survey

open access: yesThe Laryngoscope, 2023
The prevalence of voice disorders has not been explored in the context of recent trends in voice use, including voice assistant technology and increased use of teleconferencing for remote work.
Molly N. Huston, Ira Puka, M. Naunheim
semanticscholar   +1 more source

Hierarchical Multi-Class Classification of Voice Disorders Using Self-Supervised Models and Glottal Features

open access: yesIEEE Open Journal of Signal Processing, 2023
Previous studies on the automatic classification of voice disorders have mostly investigated the binary classification task, which aims to distinguish pathological voice from healthy voice.
Saska Tirronen   +2 more
semanticscholar   +1 more source

Impaired auditory discrimination and auditory-motor integration in hyperfunctional voice disorders

open access: yesScientific Reports, 2021
Hyperfunctional voice disorders (HVDs) are the most common class of voice disorders, consisting of diagnoses such as vocal fold nodules and muscle tension dysphonia.
D. Abur   +6 more
semanticscholar   +1 more source

A Comparison of Voice and Psychotherapeutic Treatments for Adults With Functional Voice Disorders: A Systematic Review

open access: yes, 2021
Objective: To examine the effect of traditional voice therapy and cognitive therapy on the voice and client-wellbeing outcomes in adults with functional voice disorders (FVD). Methods: A systematic review of English articles was conducted using Medline (
Coman, Leah   +5 more
core   +1 more source

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