Proceedings ArticleDOI
Classifier Based Early Detection of Pathological Voice
Rumana Islam,Mohammed Tarique +1 more
- pp 1-6
TLDR
A suitable set of voice features and classifiers to detect voice disability with a high accuracy is determined and an accuracy of 100% can be achieved provided proper voice feature and classifier algorithm are used.Abstract:
Voice signal processing is a popular tool to detect pathological voice in children. Voice features are first extracted from voice samples and then classifiers are used to discriminate pathological voices from normal voices. However, there is no consensus among the researchers about the voice features and the classifier algorithms that provide a high accuracy. The main contribution of this paper is to determine a suitable set of voice features and classifiers to detect voice disability with a high accuracy. In contrast to other existing works, several discriminative voice features including peaks, pitch, linear predictive coding (LPC) coefficients, Jitter, Shimmer, formants, Mel frequency cepstral coefficients (MFCCs), relative spectral amplitude (RASTA), and perceptual linear prediction (PLP) have been used. We use several classifier algorithms to discriminate pathological voices from healthy ones. We also compare the performances of these classifiers in this work. The results show that an accuracy of 100% can be achieved provided proper voice feature and classifier algorithm are used.read more
Citations
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Journal ArticleDOI
A Novel Pathological Voice Identification Technique through Simulated Cochlear Implant Processing Systems
TL;DR: In this article , a pathological voice identification system employing signal processing techniques through cochlear implant models is presented. But the method is based on the biological process for speech perception to develop this technique.
Journal ArticleDOI
Voice Pathology Detection using Convolutional Neural Networks with Electroglottographic (EGG) and Speech Signals
TL;DR: In this paper , a convolutional neural network (CNN) based automated noninvasive voice pathology detection system was proposed, which uses electroglottographic (EGG) and speech signals to detect and classify pathological voices.
Journal ArticleDOI
Chest X-Ray Images to Differentiate COVID-19 from Pneumonia with Artificial Intelligence Techniques
Rumana Islam,Mohammed Tarique +1 more
TL;DR: In this article , the authors presented an automated and non-invasive technique to discriminate COVID-19 patients from pneumonia patients using chest X-ray images and artificial intelligence, which achieved the best accuracy of 98.1%.
Proceedings ArticleDOI
Discriminating COVID-19 from Pneumonia using Machine Learning Algorithms and Chest X-ray Images
TL;DR: In this article , the authors presented automated noninvasive algorithms that can identify the X-ray images of COVID patients from that of pneumonia patients using pre-trained deep learning and machine learning algorithms, respectively.
Proceedings ArticleDOI
Discriminating COVID-19 from Pneumonia using Machine Learning Algorithms and Chest X-ray Images
Rumana Islam,Mohammed Tarique +1 more
TL;DR: In this article , the authors presented automated noninvasive algorithms that can identify the X-ray images of COVID patients from that of pneumonia patients using pre-trained deep learning and machine learning algorithms, respectively.
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