Hierarchical Classification and System Combination for Automatically Identifying Physiological and Neuromuscular Laryngeal Pathologies

Cordeiro, Hugo; Fonseca, José; Guimarães, Isabel; Meneses, Carlos

doi:10.1016/j.jvoice.2016.09.003

Cited by 24 publications

(8 citation statements)

References 17 publications

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“…In recent years machine learning based approaches have grown in popularity in voice research [25][26][27] . Machine learning was also used in combination with parameters to separate healthy from disordered voices [28][29][30] .…”

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confidence: 99%

Machine learning based identification of relevant parameters for functional voice disorders derived from endoscopic high-speed recordings

Schlegel

Kniesburges

Dürr

et al. 2020

Sci Rep

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In voice research and clinical assessment, many objective parameters are in use. However, there is no commonly used set of parameters that reflect certain voice disorders, such as functional dysphonia (FD); i.e. disorders with no visible anatomical changes. Hence, 358 high-speed videoendoscopy (HSV) recordings (159 normal females (N f), 101 FD females (FD f), 66 normal males (N M), 32 FD males (FD M)) were analyzed. We investigated 91 quantitative HSV parameters towards their significance. First, 25 highly correlated parameters were discarded. Second, further 54 parameters were discarded by using a LogitBoost decision stumps approach. This yielded a subset of 12 parameters sufficient to reflect functional dysphonia. These parameters separated groups N f vs. FD f and n M vs. FD M with fair accuracy of 0.745 or 0.768, respectively. Parameters solely computed from the changing glottal area waveform (1D-function called GAW) between the vocal folds were less important than parameters describing the oscillation characteristics along the vocal folds (2D-function called Phonovibrogram). Regularity of GAW phases and peak shape, harmonic structure and Phonovibrogram-based vocal fold open and closing angles were mainly important. This study showed the high degree of redundancy of HSV-voiceparameters but also affirms the need of multidimensional based assessment of clinical data.

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mentioning

confidence: 99%

Machine learning based identification of relevant parameters for functional voice disorders derived from endoscopic high-speed recordings

Schlegel

Kniesburges

Dürr

et al. 2020

Sci Rep

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“…In fact, they have proved to be quite useful Benmalek et al, 2017).In the extant literature, multiple authors have classified laryngeal and physiological pathologies via MFCC features with three (3) different base classifiers: the SVM, DA, and the gaussian mixture model (GMM). Yet, when applied individually, these classifiers failed toyield good results; hence, a combined classification approach has now been proposed (Cordeiro et al, 2017).…”

Section: Related Workmentioning

confidence: 99%

Deep Learning Approach for Voice Pathology Detection and Classification

Mittal

Sharma

2021

International Journal of Healthcare Information Systems and Informatics

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A non-invasive cum robust voice pathology detection and classification architecture is proposed in the current manuscript. In place of the conventional feature-based machine learning techniques, a new architecture is proposed herein which initially performs deep learning-based filtering of the input voice signal, followed by a decision-level fusion of deep learning and a non-parametric learner. The efficacy of the proposed technique is verified by performing a comparative study with very recent work on the same dataset but based on different training algorithms.The proposed architecture has five different stages.The results are recorded in terms of nine (9) different classification score indices which are – mean average Precision, sensitivity, specificity, F1 score, accuracy, error, false-positive rate, Matthews Correlation Coefficient, and the Cohen’s Kappa index. The experimental results have shown that the use of machine learning classifier can get at most 96.12% accuracy, while the proposed technique achieved the highest accuracy of 99.14% in comparison to other techniques.

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“…Recently machine learning-based approaches improved the insights in voice research [12,13,14] and to separate healthy from disordered voices [15,16,17]. Using a self-organizing map based on acoustic parameters, [15] differentiated normal from disordered voices with 0.7628 accuracy.…”

Section: Introductionmentioning

confidence: 99%

Machine-learning applied to classify flow-induced sound parameters from simulated human voice

Kraxberger¹,

Wurzinger²,

Schoder³

2022

Preprint

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Disorders of voice production have severe effects on the quality of life of the affected individuals. A simulation approach is used to investigate the cause-effect chain in voice production showing typical characteristics of voice such as sub-glottal pressure and of functional voice disorders as glottal closure insufficiency and left-right asymmetry. Therewith, 24 different voice configurations are simulated in a parameter study using a previously published hybrid aeroacoustic simulation model. Based on these 24 simulation configurations, selected acoustic parameters (HNR, CPP, ...) at simulation evaluation points are correlated with these simulation configuration details to derive characteristic insight in the flow-induced sound generation of human phonation based on simulation results. Recently, several institutions studied experimental data, of flow and acoustic properties and correlated it with healthy and disordered voice signals. Upon this, the study is a next step towards a detailed dataset definition, the dataset is small, but the definition of relevant characteristics are precise based on the existing simulation methodology of simVoice. The small datasets are studied by correlation analysis, and a Support Vector Machine classifier with RBF kernel is used to classify the representations. With the use of Linear Discriminant Analysis the dimensions of the individual studies are visualized. This allows to draw correlations and determine the most important features evaluated from the acoustic signals in front of the mouth. The GC type can be best discriminated based on CPP and boxplot visualizations. Furthermore and using the LDA-dimensionality-reduced feature space, one can best classify subglottal pressure with 91.7% accuracy, independent of healthy or disordered voice simulation parameters.

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Hierarchical Classification and System Combination for Automatically Identifying Physiological and Neuromuscular Laryngeal Pathologies

Cited by 24 publications

References 17 publications

Machine learning based identification of relevant parameters for functional voice disorders derived from endoscopic high-speed recordings

Machine learning based identification of relevant parameters for functional voice disorders derived from endoscopic high-speed recordings

Deep Learning Approach for Voice Pathology Detection and Classification

Machine-learning applied to classify flow-induced sound parameters from simulated human voice

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