AIMC Topic: Voice Disorders

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Discrimination of "hot potato voice" caused by upper airway obstruction utilizing a support vector machine.

The Laryngoscope Nov 28, 2018
OBJECTIVES/HYPOTHESIS: "Hot potato voice" (HPV) is a thick, muffled voice caused by pharyngeal or laryngeal diseases characterized by severe upper airway obstruction, including acute epiglottitis and peritonsillitis. To develop a method for determini...
Pattern Recognition, Automated Voice Voice Disorders Phonation Voice Quality Airway Obstruction Humans Support Vector Machine Signal Processing, Computer-Assisted Sound Spectrography
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A Survey on Machine Learning Approaches for Automatic Detection of Voice Disorders.

Journal of voice : official journal of the Voice Foundation Oct 11, 2018
The human voice production system is an intricate biological device capable of modulating pitch and loudness. Inherent internal and/or external factors often damage the vocal folds and result in some change of voice. The consequences are reflected in...
Voice Quality Pattern Recognition, Automated Wavelet Analysis Voice Disorders Larynx Humans Machine Learning Reproducibility of Results Diagnosis, Computer-Assisted Predictive Value of Tests Acoustics
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Demographic and Symptomatic Features of Voice Disorders and Their Potential Application in Classification Using Machine Learning Algorithms.

Folia phoniatrica et logopaedica : official organ of the International Association of Logopedics and Phoniatrics (IALP) Sep 5, 2018
BACKGROUND: Studies have used questionnaires of dysphonic symptoms to screen voice disorders. This study investigated whether the differential presentation of demographic and symptomatic features can be applied to computerized classification.
Humans Neural Networks, Computer Voice Disorders Alcohol Drinking Laryngeal Neoplasms Algorithms Symptom Assessment Voice Quality Retrospective Studies Wounds and Injuries Glottis Female Demography Aged Vocal Cord Paralysis Adult Age Factors Male Supervised Machine Learning Middle Aged Sex Factors Severity of Illness Index Smoking
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Hierarchical Classification and System Combination for Automatically Identifying Physiological and Neuromuscular Laryngeal Pathologies.

Journal of voice : official journal of the Voice Foundation Oct 12, 2016
OBJECTIVES: Speech signal processing techniques have provided several contributions to pathologic voice identification, in which healthy and unhealthy voice samples are evaluated. A less common approach is to identify laryngeal pathologies, for which...
Vocal Cord Paralysis Vocal Cords Voice Disorders Speech Production Measurement Voice Quality Signal Processing, Computer-Assisted Male Pattern Recognition, Automated Case-Control Studies Humans Edema Diagnosis, Computer-Assisted Speech Acoustics Support Vector Machine Predictive Value of Tests Databases, Factual Female
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A generalized procedure for analyzing sustained and dynamic vocal fold vibrations from laryngeal high-speed videos using phonovibrograms.

Artificial intelligence in medicine Oct 30, 2015
OBJECTIVE: This work presents a computer-based approach to analyze the two-dimensional vocal fold dynamics of endoscopic high-speed videos, and constitutes an extension and generalization of a previously proposed wavelet-based procedure. While most a...
Vocal Cords Voice Disorders Laryngoscopy Vibration Phonation Video Recording Image Interpretation, Computer-Assisted Pattern Recognition, Automated Wavelet Analysis Humans Aged Larynx Female Adult Voice Quality Reproducibility of Results Case-Control Studies Biomechanical Phenomena Time Factors Male Support Vector Machine Middle Aged Predictive Value of Tests Principal Component Analysis
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Differentiability of voice disorders through explainable AI.

Scientific reports May 25, 2025
The voice can be affected by various types of pathology. The phoniatric medical examination is the acoustic analysis, which evaluates the characteristic parameters extracted from the vocal signal. Computer-assisted decision-making systems can help sp...
Dysphonia Humans Artificial Intelligence Voice Voice Disorders
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A hybrid approach for binary and multi-class classification of voice disorders using a pre-trained model and ensemble classifiers.

BMC medical informatics and decision making May 1, 2025
Recent advances in artificial intelligence-based audio and speech processing have increasingly focused on the binary and multi-class classification of voice disorders. Despite progress, achieving high accuracy in multi-class classification remains ch...
Support Vector Machine Humans Voice Disorders Female Male Deep Learning
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Automatic GRBAS Scoring of Pathological Voices using Deep Learning and a Small Set of Labeled Voice Data.

Journal of voice : official journal of the Voice Foundation May 1, 2025
OBJECTIVES: Auditory-perceptual evaluation frameworks, such as the grade-roughness-breathiness-asthenia-strain (GRBAS) scale, are the gold standard for the quantitative evaluation of pathological voice quality. However, the evaluation is subjective; ...
Time Factors Predictive Value of Tests Signal Processing, Computer-Assisted Acoustics Observer Variation Adult Male Severity of Illness Index Deep Learning Voice Quality Automation Judgment Speech Acoustics Voice Disorders Speech Production Measurement Humans Neural Networks, Computer Female Reproducibility of Results
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Detection of Neurogenic Voice Disorders Using the Fisher Vector Representation of Cepstral Features.

Journal of voice : official journal of the Voice Foundation May 1, 2025
Neurogenic voice disorders (NVDs) are caused by damage or malfunction of the central or peripheral nervous system that controls vocal fold movement. In this paper, we investigate the potential of the Fisher vector (FV) encoding in automatic detection...
Humans Databases, Factual Female Reproducibility of Results Acoustics Sound Spectrography Middle Aged Speech Acoustics Voice Disorders Case-Control Studies Vocal Cord Paralysis Aged Speech Production Measurement Support Vector Machine Vocal Cords Young Adult Predictive Value of Tests Phonation Adult Signal Processing, Computer-Assisted Voice Quality Male Dysphonia
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Developing a smart system for binary classification of disordered voices using machine learning.

American journal of otolaryngology Jan 1, 2025
OBJECTIVES: Voice disorder is characterized by disruptions in voice quality caused by issues in vocal fold vibration during phonation. The study explored the application of machine learning, based on the Random Forest (RF) and Decision Tree (DT) mode...
Humans Machine Learning Databases, Factual ROC Curve Female Aged Decision Trees Young Adult Speech Acoustics Adult Voice Disorders Male Voice Quality Middle Aged
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