Machine Learning Approach to Dysphonia Detection

Dankovicova, Zuzana; Sovák, Dávid; Drotár, Peter; Vokorokos, Liberios

doi:10.3390/app8101927

Cited by 44 publications

(17 citation statements)

References 24 publications

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“…The SVM classifier achieved the highest accuracy by reducing the feature set to 300 using the filter FS method in the original 1560 feature. The overall classification performance based on feature selection was the highest, with 80.3% for mixed samples, 80.6% for female samples, and 86.2% for male samples [13].…”

Section: Related Workmentioning

confidence: 99%

“…As a field of study, pathological voice signal processing has always aimed to create objective and accurate classifications of voice disorders. Additionally, there have been many contributions that focus on various aspects of speech processing from feature extractions to decision support systems based on deep learning methods [13][14][15][16][17][18][19]. This section provides a brief overview of several recent findings related to the research topic of this paper.…”

Section: Related Workmentioning

confidence: 99%

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Experimental Evaluation of Deep Learning Methods for an Intelligent Pathological Voice Detection System Using the Saarbruecken Voice Database

Lee

2021

Applied Sciences

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This work is focused on deep learning methods, such as feedforward neural network (FNN) and convolutional neural network (CNN), for pathological voice detection using mel-frequency cepstral coefficients (MFCCs), linear prediction cepstrum coefficients (LPCCs), and higher-order statistics (HOSs) parameters. In total, 518 voice data samples were obtained from the publicly available Saarbruecken voice database (SVD), comprising recordings of 259 healthy and 259 pathological women and men, respectively, and using /a/, /i/, and /u/ vowels at normal pitch. Significant differences were observed between the normal and the pathological voice signals for normalized skewness (p = 0.000) and kurtosis (p = 0.000), except for normalized kurtosis (p = 0.051) that was estimated in the /u/ samples in women. These parameters are useful and meaningful for classifying pathological voice signals. The highest accuracy, 82.69%, was achieved by the CNN classifier with the LPCCs parameter in the /u/ vowel in men. The second-best performance, 80.77%, was obtained with a combination of the FNN classifier, MFCCs, and HOSs for the /i/ vowel samples in women. There was merit in combining the acoustic measures with HOS parameters for better characterization in terms of accuracy. The combination of various parameters and deep learning methods was also useful for distinguishing normal from pathological voices.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Experimental Evaluation of Deep Learning Methods for an Intelligent Pathological Voice Detection System Using the Saarbruecken Voice Database

Lee

2021

Applied Sciences

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“…In the model, this forces much more variance among the trees and eventually results in less correlation between trees. Therefore, trees that are not only learned on different sets of data (bagged) but also use different features will make decisions eventually [20,21]. The typical steps to be followed for the core working of RF are;…”

Section: ) Support Vectormentioning

confidence: 99%

Voice-Disorder Identification of Laryngeal Cancer Patients

Gour¹,

Udayashankara²,

Badakh³

et al. 2020

IJACSA

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This Previous studies have shown that much of laryngeal cancer-based work was carried out with a minimal set of linear features. Much of the work was focused on the study of larynx preservation, quality of life around radiotherapy, or surgery. The voice disorder database was not solely limited to laryngeal cancer. In the context of this, the paper proposes a noninvasive voice disorder detection of laryngeal cancer patients. The sustained vowel /a/ was recorded with 55 laryngeal cases and 55 healthy cases. Owing to the non-linearity property of the vocal cords, seven non-linear parameters along with biologically inspired 39 Mel-Frequency Cepstral Coefficients (MFCC) are extracted. This forms a laryngeal dataset of size 110X46. The wrapper method is used for better feature selection and to enhance the discriminating ability of the present work. The classification is carried out using a tuned support vector machine (SVM) with grid search and random forest (RF). The present work has shown an improved accuracy of 76.56% with SVM and 80% in the case of random forest. The forward selection of features along with the involvement of non-linear features has played a significant role in the better performance of the present system.

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“…Recently, machine learning (ML) and deep learning methods have been used to better predict voice disorders, achieving accuracy levels as high as 90% by using the acoustic parameters of jitter, shimmer, and noise-toharmonic ratio (NHR) 13 . Another study using Gaussian mixture model system reported discriminating vocal fold disorders with 99% accuracy 14 .…”

Section: Introductionmentioning

confidence: 99%

Post-stroke respiratory complications using machine learning with voice features from mobile devices

Park

Kang

et al. 2022

Preprint

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Background Abnormal voice may identify those at risk of post-stroke aspiration. This study was aimed to determine whether machine learning algorithms with voice recorded via a mobile device can accurately classify those at risk of tube feeding and post-stroke aspiration pneumonia and be used as digital biomarkers. Methods Voice samples from patients referred for swallowing disturbance in a university-affiliated hospital were collected prospectively using a mobile device. Subjects that required tube feeding were further classified to high risk of respiratory complication, based on the voluntary cough strength and abnormal chest x-ray images.Results A total of 449 samples were obtained, with 234 requiring tube feeding and 113 showing high risk of respiratory complications. The eXtreme gradient boosting multimodal models that included abnormal acoustic features and clinical variables showed high sensitivity levels of 88.7% (95% CI, 82.6–94.7) and 84.5% (95% CI, 76.9–92.1) in the classification of those at risk of tube feeding and at high risk of respiratory complications; respectively. In both cases, voice features proved to be the strongest contributing factors in these models.Conclusions Voice features may be considered as a viable digital biomarker in those at risk of respiratory complications related to post-stroke swallowing disturbance.

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Machine Learning Approach to Dysphonia Detection

Cited by 44 publications

References 24 publications

Experimental Evaluation of Deep Learning Methods for an Intelligent Pathological Voice Detection System Using the Saarbruecken Voice Database

Experimental Evaluation of Deep Learning Methods for an Intelligent Pathological Voice Detection System Using the Saarbruecken Voice Database

Voice-Disorder Identification of Laryngeal Cancer Patients

Post-stroke respiratory complications using machine learning with voice features from mobile devices

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