Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Classification of Signals01:30

Classification of Signals

1.2K
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
1.2K
Classification of Systems-I01:26

Classification of Systems-I

503
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
503
Force Classification01:22

Force Classification

2.2K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
2.2K
Classification of Illness01:17

Classification of Illness

8.4K
The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe...
8.4K
Classification of Systems-II01:31

Classification of Systems-II

430
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
430
Classification of Neurotransmitters01:30

Classification of Neurotransmitters

4.8K
Neurotransmitters play a crucial role in the communication between neurons in the autonomic nervous system. Neurons in the autonomic nervous system can be cholinergic or adrenergic depending on the neurotransmitters synthesized. Cholinergic neurons use acetylcholine as their primary neurotransmitter. This includes all the preganglionic fibers of the sympathetic and pre- and postganglionic fibers of the parasympathetic nervous systems. In addition, neurons of the somatic nervous system also use...
4.8K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

[The Role of the Cerebellum-Thalamus-Motor Cortex in Motor Learning].

Brain and nerve = Shinkei kenkyu no shinpo·2026
Same author

Characteristics of parathyroid carcinoma: A descriptive study using administrative data in Japan.

Auris, nasus, larynx·2026
Same author

Novel Surgical Approach to Posterior Nasal Neurectomy without Identifying the Posterior Nasal Nerve.

International archives of otorhinolaryngology·2026
Same author

Effects of implementing universal dysphagia screening at admission in a university hospital.

Auris, nasus, larynx·2026
Same author

Identification of stem cell marker-positive subpopulations in the vocal fold of the larynx through transcriptomic analyses.

Nature communications·2026
Same author

Proportion of middle ear surgeries feasible via transcanal endoscopic ear surgery: A multicenter study in Japan.

Auris, nasus, larynx·2026

Related Experiment Video

Updated: Dec 26, 2025

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
06:22

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

Published on: September 19, 2025

365

Classification of Voice Disorders Using a One-Dimensional Convolutional Neural Network.

Shintaro Fujimura1, Tsuyoshi Kojima2, Yusuke Okanoue2

  • 1Department of Otolaryngology-Head and Neck Surgery, Graduate School of Medicine, Kyoto University, Kyoto, Japan.

Journal of Voice : Official Journal of the Voice Foundation
|March 17, 2020
PubMed
Summary
This summary is machine-generated.

This study developed one-dimensional convolutional neural network (1D-CNN) models to objectively assess pathological voice quality using the Grade, Roughness, Breathiness, Asthenia, Strain (GRBAS) scale. The AI models achieved reliability comparable to human evaluations for voice disorder diagnosis.

Keywords:
Auditory perceptual voice analysisDeep learningGRBAS scaleOne-dimensional convolutional neural networkVoice disorder

More Related Videos

Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

818
Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
05:56

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

3.1K

Related Experiment Videos

Last Updated: Dec 26, 2025

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
06:22

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

Published on: September 19, 2025

365
Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

818
Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
05:56

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

3.1K

Area of Science:

  • Speech and Hearing Sciences
  • Artificial Intelligence in Medicine
  • Computational Linguistics

Background:

  • Auditory-perceptual voice analysis is subjective and varies between examiners.
  • Objective acoustic metrics for pathological voices are difficult to interpret and not widely used by clinicians.
  • Standardized, objective methods are needed for pathological voice quality assessment.

Purpose of the Study:

  • To develop standardized methods for discriminating Grade, Roughness, Breathiness, Asthenia, Strain (GRBAS) scale scores.
  • To utilize one-dimensional convolutional neural network (1D-CNN) models for direct, objective voice quality evaluation.
  • To establish reliable AI-driven assessment of pathological voice characteristics.

Main Methods:

  • Constructed an original dataset of 1,377 sustained phonation voice samples (/a/).
  • Collected GRBAS scale ratings from three experts, using median values as ground truth.
  • Designed and trained end-to-end 1D-CNN models on raw voice waveforms, validated using five-fold cross-validation.

Main Results:

  • The model for the G scale demonstrated balanced performance with 0.771 accuracy and 0.710 kappa.
  • The R scale model achieved 0.765 accuracy and 0.743 F1 score, with moderate agreement (kappa=0.536).
  • The S scale model showed the highest accuracy (0.883) and F1 score (0.865), despite lower kappa (0.190).

Conclusions:

  • End-to-end 1D-CNN models can reliably evaluate pathological voice quality, comparable to human judgment.
  • The quality and size of the dataset significantly impact the training and evaluation efficiency of machine learning models.
  • This approach offers a standardized, objective tool for pathological voice analysis.