Development of the Arabic Voice Pathology Database and Its Evaluation by Using Speech Features and Machine Learning Algorithms

Mesallam, Tamer A.; Farahat, Mohamed; Malki, Khalid H.; Alsulaiman, Mansour; Ali, Zulfiqar; Al-nasheri, Ahmed; Muhammad, Ghulam

doi:10.1155/2017/8783751

Cited by 74 publications

(46 citation statements)

References 45 publications

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“…Arabic Voice Pathology Database (AVPD) [41,44] was developed at the Communication and Swallowing Disorders Unit of King Abdul Aziz University Hospital, Riyadh, Saudi Arabia. The database contains recordings (366 samples: 188 healthy, 178 pathological) of sustained phonation of the vowels /a, e, o/, counting from 0-10, standardized Arabic passage, and reading three words.…”

Section: Avpd Databasementioning

confidence: 99%

“…Moreover, voice can be nowadays easily recorded using a variety of smart devices, and processed remotely using cloud technologies. From these reasons, works such as [17, [18,39], SVD -Saarbruecken Voice Database [62,44,2], AVPD -Arabic Voice Pathology Database [41,44], KM -K-means [23], RF -Random Forests [11], GMM -Gaussian Mixture Models [50], SVM -Support Vector Machines [24], NB -Naive Bayes [45], ELM -Extreme Learning Machine [30], and ANN -Artificial Neural Networks [53]. 26,44,2] focused on using signal processing techniques (to quantify vocal-manifestations of the pathology under focus) and machine learning algorithms (to automate the process of voice pathology detection) to build a system capable of accurate discrimination of healthy and pathological voices.…”

Section: Introductionmentioning

confidence: 99%

“…For the purpose of the objective voice pathology evaluation, several databases have been frequently used by the researchers. Massachusetts Eye and Ear Infirmary Database (MEEI) [18,39], Saarbruecken Voice Database (SVD) [62,44,2], and Arabic Voice Pathology Database (AVPD) [41,44] are among the most commonly used ones. More specifically, most researchers have analyzed sustained phonation of the vowel /a/, e.g.…”

Section: Introductionmentioning

confidence: 99%

“…To propose results comparable with the previously published works, we analyze voice recordings of sustained phonation of the vowel /a/ as well. However, unlike the previous works, we analyze a larger dataset composed of 4 different databases, namely: MEEI [18,39], SVD [62,44,44,2], AVPD [41,44] (these databases are commonly used by the community), and Príncipe de Asturias Database (PDA) [20,8,39]. Furthermore, to propose models capable of robust voice pathology detection, we do not restrict the dataset to only a subset of common vocal pathologies.…”

Section: Introductionmentioning

confidence: 99%

“…By using 4 different databases, we aim to increase the size of our dataset to enable exploring possibilities of using supervised deep learning techniques that de- [41,44], b) MEEI [18,39], PDA [20,8,39], and SVD [62,44,2].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Towards robust voice pathology detection

Harár

Galáž

Alonso-Hernández

et al. 2018

Neural Comput & Applic

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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.

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Section: Avpd Databasementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Towards robust voice pathology detection

Harár

Galáž

Alonso-Hernández

et al. 2018

Neural Comput & Applic

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A Generic Model for End State Prediction of Business Processes Towards Target Compliance

Khan

Ali

et al. 2019

Lecture Notes in Computer Science

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The prime concern for a business organization is to supply quality services to the customers without any delay or interruption so to establish a good reputation among the customer's and competitors. Ontime delivery of a customers order not only builds trust in the business organization but is also cost effective. Therefore, there is a need is to monitor complex business processes though automated systems which should be capable during execution to predict delay in processes so as to provide a better customer experience. This online problem has led us to develop an automated solution using machine learning algorithms so as to predict possible delay in business processes. The core characteristic of the proposed system is the extraction of generic process event log, graphical and sequence features, using the log generated by the process as it executes up to a given point in time where a prediction need to be made (referred to here as cutoff time); in an executing process this would generally be current time. These generic features are then used with Support Vector Machines, Logistic Regression, Naive Bayes and Decision trees to predict the data into on-time or delayed processes. The experimental results are presented based on real business processes evaluated using various metric performance measures such as accuracy, precision, sensitivity, specificity, F-measure and AUC for prediction as to whether the order will complete on-time when it has already been executing for a given period.

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Generative Adversarial Network-Based Semi-supervised Learning for Pathological Speech Classification

Trinh

O’Brien

2020

Statistical Language and Speech Processing

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A challenge in applying machine learning algorithms to pathological speech classification is the labelled data shortage problem. Labelled data acquisition often requires significant human effort and timeconsuming experimental design. Further, for medical applications, privacy and ethical issues must be addressed where patient data is collected. While labelled data are expensive and scarce, unlabelled data are typically inexpensive and plentiful. In this paper, we propose a semi-supervised learning approach that employs a generative adversarial network to incorporate both labelled and unlabelled data into training. We observe a promising accuracy gain with this approach compared to a baseline convolutional neural network trained only on labelled pathological speech data.

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Development of the Arabic Voice Pathology Database and Its Evaluation by Using Speech Features and Machine Learning Algorithms

Cited by 74 publications

References 45 publications

Towards robust voice pathology detection

Towards robust voice pathology detection

A Generic Model for End State Prediction of Business Processes Towards Target Compliance

Generative Adversarial Network-Based Semi-supervised Learning for Pathological Speech Classification

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