Towards developing a voice pathologies detection system

Emary, Ibrahiem M. M. El; Fezari, Mohamed; Amara, Fethi

doi:10.1134/s1064226914110059

Cited by 23 publications

(13 citation statements)

References 6 publications

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“…Results using MFCC and HMM, on German database vowels (sounds) for persons with chronic inflammation of the larynx and vocal fold nodules [19], are presented in Table V. We can see that the recognition rate is little bit lower than for healthy persons, we conclude that in this case other special features might be necessary to include on the application.…”

mentioning

confidence: 88%

vowels as HCI for Controling Mouse Cursor

Fezari

Bounouioua²,

Al-Dahoud³

2015

The 7th International Conference on Information Technology

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Abstract-HCI (Human Computer Interfaces) applications are generally based on using keyboard or joystick; in this paper we experimented the use of vowels to activate some input device such as to control the movement of mouse pointer on the screen. The control of the windows icon mouse pointer (WIMP) by voice command is currently based on using vowel utterances, this category of letters is easy to recognize and to be pronounced, especially for individuals who are physically disabled or have a partial voice disorder. So this type of MCI might be used by a category of disabled person. In addition, vowels are quite easy to model by automatic speech recognition (ASR) systems. In this work we represent the design of a system for the control of mouse cursor based on voice command, using the pronunciation of certain vowels and short words. The Mel Frequency Cepstral Coefficients (MFCCs), fundamental frequency (F0) and Formants (F1,F2) are selected as features. The TDW with Euclidian Distance and Hidden Markov Models (HMMs) have been tested as classifiers for matching components (vowels and short words). Comparison between different features and classifiers were tested and results are presented on tables, finally a GUI has been designed for user applications.

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mentioning

confidence: 88%

vowels as HCI for Controling Mouse Cursor

Fezari

Bounouioua²,

Al-Dahoud³

2015

The 7th International Conference on Information Technology

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“…Indeed, only 40 healthy and 70 pathological voices were selected through SVD database. The authors in [104] have proposed an analysis of the speech signal by applying the GMM classifier, MFCC extractor with various jitter and shimmer for the detection of the neurological disorder voice. Equations (8) and (9) are used to calculate Jitter and Shimmer in order to evaluate the percentage of the speech signal.…”

Section: Related Work Of Machine Learning In Healthcarementioning

confidence: 99%

A Survey of Voice Pathology Surveillance Systems Based on Internet of Things and Machine Learning Algorithms

et al. 2020

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The incorporation of the cloud technology with the Internet of Things (IoT) is significant in order to obtain better performance for a seamless, continuous, and ubiquitous framework. IoT has many applications in the healthcare sector, one of these applications is voice pathology monitoring. Unfortunately, voice pathology has not gained much attention, where there is an urgent need in this area due to the shortage of research and diagnosis of lethal diseases. Most of the researchers are focusing on the voice pathology and their finding is only to differentiating either the voice is normal (healthy) or pathological voice, where there is a lack of the current studies for detecting a certain disease such as laryngeal cancer. In this paper, we present an extensive review of the state-of-the-art techniques and studies of IoT frameworks and machine learning algorithms used in the healthcare in general and in the voice pathology surveillance systems in particular. Furthermore, this paper also presents applications, challenges and key issues of both IoT and machine learning algorithms in the healthcare. Finally, this paper highlights some open issues of IoT in healthcare that warrant further research and investigation in order to present an easy, comfortable and effective diagnosis and treatment of disease for both patients and doctors.

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“…The voice source parameters are analyzed to discover the influence on voice source from the database of pathological voice. El Emary et al [8] in their work classified the speech signal with MFCC, jitter and shimmer as important parameters to measure the voice disorder. The discovery of voice issues from neurological disorders was done by applying GMM method on a very small set of SVD database which comprised of 63 healthy voice and 38 pathological voices.…”

Section: Related Workmentioning

confidence: 99%

Uncertainty handling using Improvised Intuitionistic fuzzy ANN based Voice Disorder Detection

Kokila¹,

Nasira²

2019

IJRTE

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 Abstract: The voice pathology detection is one of the essential process which has to be determined in the earlier stages because it is a sign for raising health related problems. The aim of this paper is to handle the uncertainty in voice dataset due to inconsistency in extracting potential features and vagueness in dealing voice signals. The raw voice signals are preprocessed by feature extraction using meyer wavelet and potential features involved in voice disorder detection are done using sequential forward feature selection methods as voice preprocessing. This research work introduced an improvised intuitionistic fuzzy artificial neural network which enhances the process of voice disorder detection is SVD database by using analytical hierarchical processing for assigning weights and thus the complete neural network performance was fine tuned instead of assigning the weights randomly. The simulation results proved the performance of the proposed model as best by producing more promising result while comparing with ANN, PANN and Fuzzy ANN models.

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Towards developing a voice pathologies detection system

Cited by 23 publications

References 6 publications

vowels as HCI for Controling Mouse Cursor

vowels as HCI for Controling Mouse Cursor

A Survey of Voice Pathology Surveillance Systems Based on Internet of Things and Machine Learning Algorithms

Uncertainty handling using Improvised Intuitionistic fuzzy ANN based Voice Disorder Detection

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