Deep connected attention (DCA) ResNet for robust voice pathology detection and classification

2021 ◽  
Vol 70 ◽  
pp. 102973
Author(s):  
Huijun Ding ◽  
Zixiong Gu ◽  
Peng Dai ◽  
Zhou Zhou ◽  
Lu Wang ◽  
...  
Keyword(s):  
Voice Pathology Detection

Voice Pathology Detection Using Machine Learning Technique

2020 ◽  
Author(s):  
Fahad Taha AL-Dhief ◽  
Nurul Mu'azzah Abdul Latiff ◽  
Nik Noordini Nik Abd. Malik ◽  
Naseer Sabri ◽  
Marina Mat Baki ◽  
...  
Keyword(s):  
Machine Learning ◽  
Machine Learning Technique ◽  
Learning Technique ◽  
Voice Pathology Detection

scholarly journals Voice Pathology Detection Based eon Short-Term Jitter Estimations in Running Speech

2009 ◽  
Vol 61 (3) ◽  
pp. 153-170 ◽  
Author(s):  
Miltiadis Vasilakis ◽  
Yannis Stylianou
Keyword(s):  
Short Term ◽  
Voice Pathology Detection

scholarly journals Voice Pathology Detection Using Deep Learning on Mobile Healthcare Framework

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 41034-41041 ◽  
Author(s):  
Musaed Alhussein ◽  
Ghulam Muhammad
Keyword(s):  
Deep Learning ◽  
Mobile Healthcare ◽  
Voice Pathology Detection

scholarly journals Towards robust voice pathology detection

2018 ◽  
Vol 32 (20) ◽  
pp. 15747-15757 ◽  
Author(s):  
Pavol Harar ◽  
Zoltan Galaz ◽  
Jesus B. Alonso-Hernandez ◽  
Jiri Mekyska ◽  
Radim Burget ◽  
...  
Keyword(s):  
Voice Pathology Detection

scholarly journals Voice Pathology Detection Using Deep Learning: a Preliminary Study

2017 ◽  
Author(s):  
Pavol Harar ◽  
Jesus B. Alonso-Hernandezy ◽  
Jiri Mekyska ◽  
Zoltan Galaz ◽  
Radim Burget ◽  
...  
Keyword(s):  
Deep Learning ◽  
Voice Pathology Detection ◽  
Preliminary Study

scholarly journals Corrigendum: Voice Pathology Detection Using Modulation Spectrum-Optimized Metrics

2016 ◽  
Vol 4 ◽  
Author(s):  
Laureano Moro-Velázquez ◽  
Jorge Andrés Gómez-García ◽  
Juan Ignacio Godino-Llorente
Keyword(s):  
Modulation Spectrum ◽  
Voice Pathology Detection

scholarly journals Voice Pathology Detection Using the Adaptive Orthogonal Transform Method, SVM and MLP.

2021 ◽  
Vol 17 (14) ◽  
pp. 90-102
Author(s):  
Fadwa Abakarim ◽  
Abdenbi Abenaou
Keyword(s):  
Support Vector Machine ◽  
Multilayer Perceptron ◽  
Recognition System ◽  
The Other ◽  
Support Vector ◽  
Transform Method ◽  
Average Variance ◽  
Voice Pathology Detection ◽  
Orthogonal Transform ◽  
Skewness And Kurtosis

In this paper, an automatic voice pathology recognition system is realized. The special features are extracted by the Adaptive Orthogonal Transform method, and to provide their statistical properties we calculated the average, variance, skewness and kurtosis values. The classification process uses two models that are widely used as a classification method in the field of signal processing: Support Vector Machine (SVM) and Multilayer Perceptron (MLP). The proposed system is tested by using a German voice database: the Saarbruecken Voice Database (SVD). The experimental results show that the Adaptive Orthogonal Transform method works perfectly with the Multilayer Perceptron Neural Network, which achieved 98.87% accuracy. On the other hand, the combination of the Adaptive Orthogonal Transform method and Support Vector Machine reached 85.79% accuracy.

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