Improved Algorithm for Pathological and Normal Voices Identification

Sabir, Brahim; Rouda, Fatima; Khazri, Yassine; Touri, Bouzekri; Mahtar, Mohamed

doi:10.11591/ijece.v7i1.pp238-243

Cited by 11 publications

(13 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[36,17,26], etc. From the voice pathologies point of view, most researchers restricted the dataset to a limited set of pathologies [7,43,14,25,51,44,5,3,4,2].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Towards robust voice pathology detection

Harár

Galáž

Alonso-Hernández

et al. 2018

Neural Comput & Applic

View full text Add to dashboard Cite

Automatic objective non-invasive detection of pathological voice based on computerized analysis of acoustic signals can play an important role in early diagnosis, progression tracking and even effective treatment of pathological voices. In search towards such a robust voice pathology detection system we investigated 3 distinct classifiers within supervised learning and anomaly detection paradigms. We conducted a set of experiments using a variety of input data such as raw waveforms, spectrograms, mel-frequency cepstral coefficients (MFCC) and conventional acoustic (dysphonic) features (AF). In comparison with previously published works, this article is the first to utilize combination of 4 different databases comprising normophonic and pathological recordings of sustained phonation of the vowel /a/ unrestricted to a subset of vocal pathologies. Furthermore, to our best knowledge, this article is the first to explore gradient boosted trees and deep learning for this application. The following best classification performances measured by F1 score on dedicated test set were achieved: XGBoost (0.733) using AF and MFCC, DenseNet (0.621) using MFCC, and Isolation Forest (0.610) using AF. Even though these results are of exploratory character, conducted experiments do show promising potential of gradient boosting and deep learning methods to robustly detect voice pathologies.

show abstract

“…[36,17,26], etc. From the voice pathologies point of view, most researchers restricted the dataset to a limited set of pathologies [7,43,14,25,51,44,5,3,4,2].…”

Section: Introductionmentioning

confidence: 99%

“…Next, conventional and clinically interpretable [10]) acoustic features were usually computed prior to pathology detection [43,14,51]. The acoustic features such as multidimensional voice program parameters (MDVP) [4], mel-frequency cepstral coefficients (MFCC) [52], glottalto-noise excitation ratio (GNE) [42], etc.…”

Section: Introductionmentioning

confidence: 99%

Towards robust voice pathology detection

Harár

Galáž

Alonso-Hernández

et al. 2018

Neural Comput & Applic

View full text Add to dashboard Cite

show abstract

“…In [102], the authors argue that although there are many research works published to detect pathological voices; however, only a few of them deal with the severity of estimation of voice disabilities. The authors present an automatic classifier using an acoustical measurement of sustained vowel '/a/' and pattern recognition tool based on neural networks.…”

Section: B the Multiple Featuresmentioning

confidence: 99%

A Survey on Signal Processing Based Pathological Voice Detection Techniques

2020

View full text Add to dashboard Cite

Voice disability is a barrier to effective communication. Around 1.2% of the World's population is facing some form of voice disability. Surgical procedures namely laryngoscopy, laryngeal electromyography, and stroboscopy are used for voice disability diagnosis. Researchers and practitioners have been working to find alternatives to these surgical procedures. Voice sample based diagnosis is one of them. The major steps followed by these works are (a) to extract voice features from voice samples and (b) to discriminate pathological voices from normal voices by using a classifier algorithm. However, there is no consensus about the voice feature and the classifier algorithm that can provide the best accuracy in screening voice disability. Moreover, some of the works use multiple voice features and multiple classifiers to ensure high reliability. In this paper, we address these issues. The motivation of the work is to address the need for non-invasive signal processing techniques to detect voice disability in the general population. This paper conducts a survey related to voice disability detection methods. The paper contains two main parts. In the first part, we present background information including causes of voice disability, current procedures and practices, voice features, and classifiers. In the second part, we present a comprehensive survey work on voice disability detection algorithms. The issues and challenges related to the selection of voice feature and classifier algorithms have been addressed at the end of this paper. INDEX TERMS Algorithms, issues and challenges, signal processing, surgical methods, survey, voice disability, voice features.

show abstract

“…With the development of image processing and artificial intelligence technology, deep learning has made great progress in pathological analysis in recent years. For example, some studies on pathological and normal voice identification [1], gastric cancer diagnosis [2], pathological retina images segmentation [3], gait analysis [4], and pathological cells recognition [5] have got high recognition accuracy. However, there still exist certain problems in the pathological diagnosis of the current artificial intelligence technology and the data resources are the most important one [6].…”

Section: Introductionmentioning

confidence: 99%

A Multidimensional Choledoch Database and Benchmarks for Cholangiocarcinoma Diagnosis

Zhang

et al. 2019

IEEE Access

View full text Add to dashboard Cite

Histopathological examination is very important for diseases diagnosis and treatment. With the development of artificial intelligence, more and more pathological databases have been reported for histopathological diagnosis because database is quite crucial for the validation and testing of feature extraction, statistical analysis and deep learning algorithms. However, most of these databases are either gray images or RGB color images of tissue sections contain limited information of samples which limited the performance of most current deep learning algorithms. There are few publicly available pathological databases that include more than two modalities for the same subject. This paper introduces a database for both microscopy hyperspectral and color images of cholangiocarcinoma, including 880 scenes from 174 individuals, among which 689 scenes are samples with part of cancer areas, 49 scenes full of cancer areas, and 142 scenes without cancer areas. In addition, all cancer areas have been precisely labeled by experienced pathologists. The contributions of this work: a) A comprehensive and up-to-date review on pathological imaging systems and databases; b) Detailed description of the proposed the multidimensional Choledoch Database and login method; c) The multidimensional Choledoch Database has been published and can be downloaded after registration and made an entry on the website.

show abstract

Improved Algorithm for Pathological and Normal Voices Identification

Cited by 11 publications

References 15 publications

Towards robust voice pathology detection

Towards robust voice pathology detection

A Survey on Signal Processing Based Pathological Voice Detection Techniques

A Multidimensional Choledoch Database and Benchmarks for Cholangiocarcinoma Diagnosis

Contact Info

Product

Resources

About