Voice Pathology Detection Using Machine Learning Technique

Recent proposed researches have witnessed that voice pathology detection systems can effectively contribute to the voice disorders assessment and provide early detection of voice pathologies. These systems used machine learning techniques which are considered as very promising tools in the detection of voice pathologies. However, most proposed systems in the detection of voice disorder utilized limited database. Furthermore, low accuracy rate is still the one of the most challenging issues for these techniques. This paper presents a voice pathology detection system using Online Sequential Extreme Learning Machine (OSELM) to classify the voice signal into healthy or pathological. In this work, the voice features are extracted by using Mel-Frequency Cepstral Coefficient (MFCC). The voice samples for the vowel /a/ were collected equally from Saarbrücken voice database (SVD). The proposed method is evaluated by three widely used measurements which are accuracy, sensitivity and specificity. The obtained results show that the maximum accuracy, sensitivity and specificity are 85%, 87% and 87%, respectively. According to the experimental results, the performance of OSELM algorithm is able to differentiate healthy and pathological voices effectively.

[1]  I. Elamvazuthi,et al.  Voice Recognition Algorithms using Mel Frequency Cepstral Coefficient (MFCC) and Dynamic Time Warping (DTW) Techniques , 2010, ArXiv.

[2]  Paavo Alku,et al.  Analysis and Detection of Pathological Voice Using Glottal Source Features , 2020, IEEE Journal of Selected Topics in Signal Processing.

[3]  Jirí Mekyska,et al.  Voice Pathology Detection Using Deep Learning: a Preliminary Study , 2017, 2017 International Conference and Workshop on Bioinspired Intelligence (IWOBI).

[4]  Ghulam Muhammad,et al.  Development of the Arabic Voice Pathology Database and Its Evaluation by Using Speech Features and Machine Learning Algorithms , 2017, Journal of healthcare engineering.

[5]  Adnane Cherif,et al.  Speech recognition system based on short-term cepstral parameters, feature reduction method and Artificial Neural Networks , 2016, 2016 2nd International Conference on Advanced Technologies for Signal and Image Processing (ATSIP).

[6]  Muhammad Ghulam,et al.  Smart Health Solution Integrating IoT and Cloud: A Case Study of Voice Pathology Monitoring , 2017, IEEE Communications Magazine.

[7]  Fahad Taha Al-Dhief,et al.  A Survey of Voice Pathology Surveillance Systems Based on Internet of Things and Machine Learning Algorithms , 2020, IEEE Access.

[8]  Imen Hammami,et al.  Pathological voices detection using Support Vector Machine , 2016, 2016 2nd International Conference on Advanced Technologies for Signal and Image Processing (ATSIP).

[9]  Mazin Abed Mohammed,et al.  Voice Pathology Detection and Classification Using Convolutional Neural Network Model , 2020, Applied Sciences.

[10]  Vikas Mittal,et al.  Glottal Signal Analysis for Voice Pathology , 2019, 2019 2nd International Conference on Innovations in Electronics, Signal Processing and Communication (IESC).

[11]  Fahad Taha Al-Dhief,et al.  Spoken language identification based on the enhanced self-adjusting extreme learning machine approach , 2018, PloS one.

[12]  Musaed Alhussein,et al.  Voice Pathology Detection Using Deep Learning on Mobile Healthcare Framework , 2018, IEEE Access.

[13]  Igor E. Kheidorov,et al.  Vocal fold pathology detection using modified wavelet-like features and support vector machines , 2007, 2007 15th European Signal Processing Conference.

[14]  Lee Luan Ling,et al.  Kullback-Leibler divergence and sample skewness for pathological voice quality assessment , 2020, Biomed. Signal Process. Control..

[15]  Musatafa Abbas Abbood Albadr,et al.  Spoken Language Identification Based on Particle Swarm Optimisation–Extreme Learning Machine Approach , 2020, Circuits, Systems, and Signal Processing.

[16]  György Szaszák,et al.  Artificial Neural Network and SVM based Voice Disorder Classification , 2019, 2019 10th IEEE International Conference on Cognitive Infocommunications (CogInfoCom).

[17]  Eduardo Lleida,et al.  Voice Pathology Detection on the Saarbrücken Voice Database with Calibration and Fusion of Scores Using MultiFocal Toolkit , 2012, IberSPEECH.

[18]  Narasimhan Sundararajan,et al.  On-Line Sequential Extreme Learning Machine , 2005, Computational Intelligence.

[19]  Mohamed Fezari,et al.  An Improved GMM-SVM System based on Distance Metric for Voice Pathology Detection , 2016 .

[20]  Mohamed Fezari,et al.  Towards developing a voice pathologies detection system , 2014 .

[21]  Bogdan Woldert-Jokisz,et al.  Saarbruecken Voice Database , 2007 .

[22]  Fahad Taha Al-Dhief,et al.  Spoken language identification based on optimised genetic algorithm–extreme learning machine approach , 2019, International Journal of Speech Technology.

[23]  Musatafa Abbas Abbood Albadr,et al.  Extreme learning machine: A review , 2017 .

[24]  Ghulam Muhammad,et al.  Voice Pathology Detection and Classification Using Auto-Correlation and Entropy Features in Different Frequency Regions , 2018, IEEE Access.

[25]  Musaed Alhussein,et al.  Automatic Voice Pathology Monitoring Using Parallel Deep Models for Smart Healthcare , 2019, IEEE Access.

[26]  Muhammad Ghulam,et al.  Edge Computing with Cloud for Voice Disorder Assessment and Treatment , 2018, IEEE Communications Magazine.

[27]  Sridhar Krishnan,et al.  Augmenting Dysphonia Voice Using Fourier-based Synchrosqueezing Transform for a CNN Classifier , 2019, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[28]  Juan Ignacio Godino-Llorente,et al.  Automatic Detection of Laryngeal Pathologies in Records of Sustained Vowels by Means of Mel-Frequency Cepstral Coefficient Parameters and Differentiation of Patients by Sex , 2009, Folia Phoniatrica et Logopaedica.

[29]  S. Labidi,et al.  Voice Pathologies Classification and Detection Using EMD-DWT Analysis Based on Higher Order Statistic Features , 2020 .