A survey on machine learning approaches for automatic detection of voice disorders
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 …
pitch and loudness. Inherent internal and/or external factors often damage the vocal folds …
Voice pathology detection and classification by adopting online sequential extreme learning machine
In the last decade, the implementation of machine learning algorithms in the analysis of
voice disorder is paramount in order to provide a non-invasive voice pathology detection by …
voice disorder is paramount in order to provide a non-invasive voice pathology detection by …
Classification of voice disorders using a one-dimensional convolutional neural network
S Fujimura, T Kojima, Y Okanoue, K Shoji, M Inoue… - Journal of Voice, 2022 - Elsevier
Objectives Auditory-perceptual voice analysis is a standard method for quantifying
pathological voice quality, but perceptual ratings are based on subjective evaluations and …
pathological voice quality, but perceptual ratings are based on subjective evaluations and …
Gammatone spectral latitude features extraction for pathological voice detection and classification
C Zhou, Y Wu, Z Fan, X Zhang, D Wu, Z Tao - Applied Acoustics, 2022 - Elsevier
To improve the performance of pathological voice detection and classification, gammatone
spectral latitude (GTSL) features were proposed. GTSL features are inspired by the …
spectral latitude (GTSL) features were proposed. GTSL features are inspired by the …
[HTML][HTML] Voice pathology detection using convolutional neural networks with electroglottographic (EGG) and speech signals
This paper presents a convolutional neural network (CNN) based automated noninvasive
voice pathology detection system. The proposed system functions in two steps. First, it …
voice pathology detection system. The proposed system functions in two steps. First, it …
Voice disorder recognition using machine learning: a scoping review protocol
Introduction Over the past decade, several machine learning (ML) algorithms have been
investigated to assess their efficacy in detecting voice disorders. Literature indicates that ML …
investigated to assess their efficacy in detecting voice disorders. Literature indicates that ML …
The relationship between auditory-perceptual rating scales and objective voice measures in children with voice disorders
RB Fujiki, SL Thibeault - American Journal of Speech-Language Pathology, 2021 - ASHA
Purpose The purpose of this study was to determine concurrent validity of the Grade,
Roughness, Breathiness, Asthenia, and Strain (GRBAS) and Consensus Auditory …
Roughness, Breathiness, Asthenia, and Strain (GRBAS) and Consensus Auditory …
Detection of different voice diseases based on the nonlinear characterization of speech signals
This work describes a novel methodology to characterize voice diseases by using nonlinear
dynamics, considering different complexity measures that are mainly based on the analysis …
dynamics, considering different complexity measures that are mainly based on the analysis …
Automatic GRBAS scoring of pathological voices using deep learning and a small set of labeled voice data
S Hidaka, Y Lee, M Nakanishi, K Wakamiya… - Journal of Voice, 2022 - Elsevier
Objectives Auditory-perceptual evaluation frameworks, such as the grade-roughness-
breathiness-asthenia-strain (GRBAS) scale, are the gold standard for the quantitative …
breathiness-asthenia-strain (GRBAS) scale, are the gold standard for the quantitative …
Machine learning based estimation of hoarseness severity using sustained vowels
T Schraut, A Schützenberger, T Arias-Vergara… - The Journal of the …, 2024 - pubs.aip.org
Auditory perceptual evaluation is considered the gold standard for assessing voice quality,
but its reliability is limited due to inter-rater variability and coarse rating scales. This study …
but its reliability is limited due to inter-rater variability and coarse rating scales. This study …