A Survey on Machine Learning Approaches for Automatic Detection of Voice Disorders

Published:October 11, 2018DOI:https://doi.org/10.1016/j.jvoice.2018.07.014

      Summary

      The human voice production system is an intricate biological device capable of modulating pitch and loudness. Inherent internal and/or external factors often damage the vocal folds and result in some change of voice. The consequences are reflected in body functioning and emotional standing. Hence, it is paramount to identify voice changes at an early stage and provide the patient with an opportunity to overcome any ramification and enhance their quality of life. In this line of work, automatic detection of voice disorders using machine learning techniques plays a key role, as it is proven to help ease the process of understanding the voice disorder. In recent years, many researchers have investigated techniques for an automated system that helps clinicians with early diagnosis of voice disorders. In this paper, we present a survey of research work conducted on automatic detection of voice disorders and explore how it is able to identify the different types of voice disorders. We also analyze different databases, feature extraction techniques, and machine learning approaches used in these research works.

      Key Words

      To read this article in full you will need to make a payment
      Subscribe to Journal of Voice
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect

      References

        • Titze I.R.
        • Verdolini K.
        Vocology: The Science and Practice of Voice Habilitation.
        National Center for Voice and Speech, Salt Lake City, UT2012
        • Dejonckere P
        • Bradeley P
        • Clemente P
        • et al.
        A basic protocol for functional assessment of voice pathology, especially for investigating the efficacy of (phonosurgical) treatments and evaluating new assessment techniques.
        Eur Arch Otorhinolaryngol. 2001; 258: 77-82
        • NIDCD
        2012-2016 Strategic Plan,Vol. 2.. Bethesda, MD: National Institute on Deafness and Other Communication Disorders (NIDCD), U.S. Department of Health and Human Services, Bethesda, MD2012
      1. American Speech-Language-Hearing Association and Others. “Council for clinical certification in audiology and speech-language pathology,” Retrieved September, vol. 15, 2015.

        • Verdolini K.
        • Rosen C.A.
        • Branski R.C.
        • et al.
        Classification Manual for Voice Disorders—I. Special Interest Division 3: Voice and Voice Disorders.
        Lawrence Erlbaum Associates, Inc, 2006
        • Titze I.R.
        • Švec J.G.
        • Popolo P.S.
        Vocal dose measures: quantifying accumulated vibration exposure in vocal fold tissues.
        J Speech Lang Hear Res. 2003; 46: 919-932https://doi.org/10.1044/1092-4388(2003/072)
        • Boominathan P.
        • Samuel J.
        • Arunachalam R.
        • et al.
        Multiparametric voice assessment: Sri Ramachandra University protocol.
        Indian J Otolaryngol Head Neck Surg. 2014; 66: 246-251
        • Al-nasheri A.
        • Muhammad G.
        • Alsulaiman M.
        • et al.
        An investigation of multidimensional voice program parameters in three different databases for voice pathology detection and classification.
        J Voice. 2017; 31 (113.e9–113.e18)
        • Kasuya H.
        • Ogawa S.
        • Kikuchi Y.
        • et al.
        An acoustic analysis of pathological voice and its application to the evaluation of laryngeal pathology.
        Speech Commun. 1986; 5: 171-181
        • Sonu
        • Sharma R.K
        Disease detection using analysis of voice parameters.
        Int J Comput Sci Commun Technol. 2012; 4: 6-10
        • Al-nasheri A.
        • Ali Z.
        • Muhammad G.
        • et al.
        Voice pathology detection with MDVP parameters using Arabic voice pathology database.
        in: IEEE 5th National Symposium on Information Technology: Towards New Smart World (NSITNSW). 2015: 1-5
        • Rabiner L.R.
        • Juang B.-H.
        Fundamentals of Speech Recognition.
        Prentice Hall, 1993
        • Slaney M.
        Toolbox: a Matlab toolbox for auditory modeling. Work Technical Report,.
        Interval Research Corporation, 1998: 29-32
        • Atal B.S.
        • Hanauer S.L.
        Speech analysis and synthesis by linear prediction of the speech wave.
        J Acoust Soc Am. 1971; 50: 637-655
        • Kohler M.
        • Vellasco M.M.
        • Cataldo E.
        Analysis and classification of voice pathologies using glottal signal parameters.
        J Voice. 2016; 30: 549-556
        • Kantardzic M.
        Data Reduction.
        John Wiley & Sons, Inc, 2003: 53-86
        • Deller J.R.
        • Anderson D.J.
        Automatic classification of laryngeal dysfunction using the roots of the digital inverse filter.
        IEEE Trans Biomed Eng. 1980; 12: 714-721
        • Childers D.G.
        • Bae K.S.
        Detection of laryngeal function using speech and electroglottographic data.
        IEEE Trans Biomed Eng. 1992; 39: 19-25
        • Cairns D.A.
        • Hansen J.H.
        • Riski J.E.
        A noninvasive technique for detecting hypernasal speech using a nonlinear operator.
        IEEE Trans Biomed Eng. 1996; 43: 35
        • Accardo A.P.
        • Mumolo E.
        An algorithm for the automatic differentiation between the speech of normals and patients with Friedreich's ataxia based on the short-time fractal dimension.
        Comput Biol Med. 1998; 28: 75-89
        • Parsa V.
        • Jamieson D.G.
        Identification of pathological voices using glottal noise measures.
        J Speech Lang Hear Res. 2000; 43: 469-485
        • Hadjitodorov S.
        • Boyanov B.
        • Teston B.
        Laryngeal pathology detection by means of class- specific neural maps.
        IEEE Trans Inf Technol Biomed. 2000; 4: 68-73
        • de Oliveira Rosa M.
        • Pereira J.C.
        • Grellet M.
        Adaptive estimation of residue signal for voice pathology diagnosis.
        IEEE Trans Biomed Eng. 2000; 47: 96-104
        • Watts C.R.
        • Clark R.
        • Early S.
        Acoustic measures of phonatory improvement secondary to treatment by oral corticosteroids in a professional singer: a case report.
        J Voice. 2001; 15: 115-121
        • Guido R.C.
        • Pereira J.C.
        • Fonseca E.
        • et al.
        Trying different wavelets on the search for voice disorders sorting.
        in: Proceedings of the Thirty-Seventh Southeastern Symposium on System Theory, 2005 (SSST'05). IEEE, 2005: 495-499
        • Umapathy K.
        • Krishnan S.
        • Parsa V.
        • et al.
        Discrimination of pathological voices using a time-frequency approach.
        IEEE Trans Biomed Eng. 2005; 52: 421-430
        • Zhang Y.
        • Jiang J.J.
        • Biazzo L.
        • et al.
        Perturbation and nonlinear dynamic analyses of voices from patients with unilateral laryngeal paralysis.
        J Voice. 2005; 19: 519-528
        • Neto B.G.A.
        • Fechine J.M.
        • Costa S.C.
        • et al.
        Feature estimation for vocal fold edema detection using short-term cepstral analysis.
        in: Proceedings of the 7th IEEE International Conference on Bioinformatics and Bioengineering, 2007 (BIBE 2007). IEEE, 2007: 1158-1162
        • Gómez-Vilda P.
        • Fernández-Baillo R
        • Nieto A.
        • et al.
        Evaluation of voice pathology based on the estimation of vocal fold biomechanical parameters.
        J Voice. 2007; 21: 450-476
        • Gómez-Vilda P.
        • Fernández-Baillo R.
        • Rodellar-Biarge V.
        • et al.
        Glottal source biometrical signature for voice pathology detection.
        Speech Commun. 2009; 51: 759-781
        • Zhang Y.
        • Jiang J.J.
        Acoustic analyses of sustained and running voices from patients with laryngeal pathologies.
        J Voice. 2008; 22: 1-9
        • Fontes A.I.
        • Souza P.T.
        • Neto A.D.
        • et al.
        Classification system of pathological voices using correntropy.
        Math Probl Eng. 2014; 2014: 1-7
        • Levinson S.E.
        • Rabiner L.R.
        • Sondhi M.M.
        An introduction to the application of the theory of probabilistic functions of a Markov process to automatic speech recognition.
        Bell Syst Tech J. 1983; 62: 1035-1074
        • Poritz A.B.
        Hidden Markov models: a guided tour.
        in: Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP). 1988: 7-13
        • Rabiner L.R.
        • Juang B.H.
        Speech recognition: statistical methods.
        Encyclopedia of Language & Linguistics. 2nd ed. 2006: 1-18
        • Rao P.V.S.
        VOICE: an integrated speech recognition synthesis system for the Hindi language.
        Speech Commun. 1993; 13: 197-205
        • Gavidia-Ceballos L.
        • Hansen J.H.
        Direct speech feature estimation using an iterative EM algorithm for vocal fold pathology detection.
        IEEE Trans Biomed Eng. 1996; 43: 373-383
        • Arias-Londoño J.D.
        • Godino-Llorente J.I.
        • Sáenz-Lechón N.
        • et al.
        An improved method for voice pathology detection by means of a HMM-based feature space transformation.
        Pattern Recognit. 2010; 43: 3100-3112
        • Muhammad G.
        • Mesallam T.A.
        • Malki K.H.
        • et al.
        Multidirectional regression (MDR)-based features for automatic voice disorder detection.
        J Voice. 2012; 26 (817.e19–817.e27)
        • Ali Z.
        • Elamvazuthi I.
        • Alsulaiman M.
        • et al.
        Automatic voice pathology detection with running speech by using estimation of auditory spectrum and cepstral coefficients based on the all-pole model.
        J Voice. 2015; 30 (757–e7)
        • Ali Z.
        • Muhammad G.
        • Alhamid M.F.
        An automatic health monitoring system for patients suffering from voice complications in smart cities.
        IEEE Access. 2017; 5: 3900-3908
        • Vapnik V.
        The Nature of Statistical Learning Theory.
        Springer Science & Business Media, 2013
        • Behroozmand R.
        • Almasganj F.
        Optimal selection of wavelet-packet-based features using genetic algorithm in pathological assessment of patients’ speech signal with unilateral vocal fold paralysis.
        Comput Biol Med. 2007; 37: 474-485
        • Markaki M.
        • Stylianou Y.
        Voice pathology detection and discrimination based on modulation spectral features.
        IEEE Trans Audio Speech Lang Process. 2011; 19: 1938-1948
        • Saeedi N.E.
        • Almasganj F.
        • Torabinejad F.
        Support vector wavelet adaptation for pathological voice assessment.
        Comput Biol Med. 2011; 41: 822-828
        • Arjmandi M.K.
        • Pooyan M.
        An optimum algorithm in pathological voice quality assessment using wavelet-packet-based features, linear discriminant analysis and support vector machine.
        Biomed Signal Process Control. 2012; 7: 3-19
        • Uloza V.
        • Verikas A.
        • Bacauskiene M.
        • et al.
        Categorizing normal and pathological voices: automated and perceptual categorization.
        J Voice. 2011; 25: 700-708
        • Muhammad G.
        • Melhem M.
        Pathological voice detection and binary classification using MPEG-7 audio features.
        Biomed Signal Process Control. 2014; 11: 1-9
        • Saidi P.
        • Almasganj F.
        Voice disorder signal classification using m-band wavelets and support vector machine.
        Circuits Syst Signal Process. 2015; 34: 2727-2738
        • Orozco-Arroyave J.R.
        • Belalcazar-Bolanos E.A.
        • Arias-Londoño J.D.
        • et al.
        Characterization methods for the detection of multiple voice disorders: neurological, functional, and laryngeal diseases.
        IEEE J Biomed Health Inf. 2015; 19: 1820-1828
        • Benba A.
        • Jilbab A.
        • Hammouch A.
        Analysis of multiple types of voice recordings in cepstral domain using MFCC for discriminating between patients with Parkinson's disease and healthy people.
        Int J Speech Technol. 2016; 19: 449-456
        • Benba A.
        • Jilbab A.
        • Hammouch A.
        Discriminating between patients with Parkinson's and neurological diseases using cepstral analysis.
        IEEE Trans Neural Syst Rehabil Eng. 2016; 24: 1100-1108
        • Ali Z.
        • Alsulaiman M.
        • Elamvazuthi I.
        • et al.
        Voice pathology detection based on the modified voice contour and SVM.
        Biol Inspired Cognit Archit. 2016; 15: 10-18
        • Ali Z.
        • Elamvazuthi I.
        • Alsulaiman M.
        • et al.
        Detection of voice pathology using fractal dimension in a multiresolution analysis of normal and disordered speech signals.
        J Med Syst. 2016; 40: 20
        • Muhammad G.
        • Altuwaijri G.
        • Alsulaiman M.
        • et al.
        Automatic voice pathology detection and classification using vocal tract area irregularity.
        Biocybern Biomed Eng. 2016; 36: 309-317
        • Al-nasheri A.
        • Muhammad G.
        • Alsulaiman M.
        • et al.
        Investigation of voice pathology detection and classification on different frequency regions using correlation functions.
        J Voice. 2017; 31: 3-15
        • Cordeiro H.
        • Fonseca J.
        • Guimarães I.
        • et al.
        Hierarchical classification and system combination for automatically identifying physiological and neuromuscular laryngeal pathologies.
        J Voice. 2017; 31 (384.e9–384.e14)
        • Amami R.
        • Smiti A.
        An incremental method combining density clustering and support vector machines for voice pathology detection.
        Comput Electr Eng. 2017; 57: 257-265
        • Al-Nasheri A.
        • Muhammad G.
        • Alsulaiman M.
        • et al.
        Voice pathology detection and classification using auto- correlation and entropy features in different frequency regions.
        IEEE Access. 2018; 6: 6961-6974
        • Ritchings R.T.
        • McGillion M.
        • Moore C.J.
        Pathological voice quality assessment using artificial neural networks.
        Med Eng Phys. 2002; 24: 561-564
        • Godino-Llorente J.I.
        • Gomez-Vilda P.
        Automatic detection of voice impairments by means of short-term cepstral parameters and neural network based detectors.
        IEEE Trans Biomed Eng. 2004; 51: 380-384
        • Crovato C.D.P.
        • Schuck A.
        The use of wavelet packet transform and artificial neural networks in analysis and classification of dysphonic voices.
        IEEE Trans Biomed Eng. 2007; 54: 1898-1900
        • Hariharan M.
        • Paulraj M.P.
        • Yaacob S.
        Detection of vocal fold paralysis and edema using time-domain features and probabilistic neural network.
        Int J Biomed Eng Technol. 2011; 6: 46-57
        • Akbari A.
        • Arjmandi M.K.
        An efficient voice pathology classification scheme based on applying multi-layer linear discriminant analysis to wavelet packet-based features.
        Biomed Signal Process Control. 2014; 10: 209-223
        • Chen H.L.
        • Wang G.
        • Ma C.
        • et al.
        An efficient hybrid kernel extreme learning machine approach for early diagnosis of Parkinson׳ s disease.
        Neurocomputing. 2016; 184: 131-144
        • Huang G.B.
        • Zhu Q.Y.
        • Siew C.K.
        Extreme learning machine: theory and applications.
        Neurocomputing. 2006; 70: 489-501
        • Teixeira J.P.
        • Fernandes P.O.
        • Alves N.
        Vocal acoustic analysis—classification of dysphonic voices with artificial neural networks.
        Proc Comput Sci. 2017; 121: 19-26
        • Alpaydin E.
        Introduction to Machine Learning.
        MIT Press, 2014
        • Hadjitodorov S.
        • Mitev P.
        A computer system for acoustic analysis of pathological voices and laryngeal diseases screening.
        Med Eng Phys. 2002; 24: 419-429
        • Shama K.
        • Cholayya N.U.
        Study of harmonics-to-noise ratio and critical-band energy spectrum of speech as acoustic indicators of laryngeal and voice pathology.
        EURASIP J Appl Signal Process. 2007; 2007: 50
        • Hemmerling D.
        • Skalski A.
        • Gajda J.
        Voice data mining for laryngeal pathology assessment.
        Comput Biol Med. 2016; 69: 270-276
        • Kaleem M.
        • Ghoraani B.
        • Guergachi A.
        • Krishnan S.
        Pathological speech signal analysis and classification using empirical mode decomposition.
        Med Biol Eng Comput. 2013; 51: 811-821
        • Ghoraani B.
        • Krishnan S.
        A joint time-frequency and matrix decomposition feature extraction methodology for pathological voice classification.
        EURASIP J Adv Signal Process. 2009; 2009928974
        • Woźniak M.
        • Graña M.
        • Corchado E.
        A survey of multiple classifier systems as hybrid systems.
        Inf Fusion. 2014; 16: 3-17
        • Gelzinis A.
        • Verikas A.
        • Bacauskiene M.
        Automated speech analysis applied to laryngeal disease categorization.
        Comput Methods Prog Biomed. 2008; 91: 36-47
        • Das R.
        A comparison of multiple classification methods for diagnosis of Parkinson disease.
        Expert Syst Appl. 2010; 37: 1568-1572
        • Arias-Londono J.D.
        • Godino-Llorente J.I.
        • Sáenz-Lechón N.
        • et al.
        Automatic detection of pathological voices using complexity measures, noise parameters, and mel-cepstral coefficients.
        IEEE Trans Biomed Eng. 2011; 58: 370-379
        • Arjmandi M.K.
        • Pooyan M.
        • Mikaili M.
        • et al.
        Identification of voice disorders using long-time features and support vector machine with different feature reduction methods.
        J Voice. 2011; 25: e275-e289
        • Hariharan M.
        • Polat K.
        • Sindhu R.
        A new hybrid intelligent system for accurate detection of Parkinson's disease.
        Comput Methods Prog Biomed. 2014; 113: 904-913
        • Jothilakshmi S.
        Automatic system to detect the type of voice pathology.
        Appl Soft Comput. 2014; 21: 244-249
        • Holi M.S.
        Wavelet transform features to hybrid classifier for detection of neurological-disordered voices.
        J Clin Eng. 2017; 42: 89-98
        • Weeks M.
        Digital Signal Processing Using Matlab and Wavelets.
        Infinity Science Press LLC, 2006
        • Martinez C.E.
        • Rufiner H.L.
        Acoustic analysis of speech for detection of laryngeal pathologies.
        in: Proceedings of 22nd Annual IEEE International Conference on Engineering in Medicine and Biology Society. 3. IEEE, 2000: 2369-2372
        • Dibazar A.A.
        • Narayanan S.
        • Berger T.W.
        Feature analysis for automatic detection of pathological speech.
        in: Proceedings of the Second Joint Engineering in Medicine and Biology, 2002. 24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society EMBS/BMES Conference. 1. IEEE, 2002: 182-183
        • Nayak J.
        • Bhat P.S.
        Identification of voice disorders using speech samples.
        in: TENCON 2003. Conference on Convergent Technologies for the Asia-Pacific Region. 3. IEEE, 2003: 951-953
        • Ananthakrishna T.
        • Shama K.
        • Niranjan U.C.
        k-means nearest neighbor classifier for voice pathology.
        in: Proceedings of the IEEE INDICON 2004 First India Annual Conference. IEEE, 2004: 352-354
        • Behroozmand R.
        • Almasganj F.
        Comparison of neural networks and support vector machines applied to optimized features extracted from patients' speech signal for classification of vocal fold inflammation.
        in: Proceedings of the Fifth IEEE International Symposium on Signal Processing and Information Technology. IEEE, 2005: 844-849
        • Fonseca E.S.
        • Guido R.C.
        • Silvestre A.C.
        • et al.
        Discrete wavelet transform and support vector machine applied to pathological voice signals identification.
        in: Seventh IEEE International Symposium on Multimedia. IEEE, 2005: 5
        • Godino-Llorente J.I.
        • Gómez-Vilda P.
        • Sáenz-Lechón N.
        • et al.
        Discriminative methods for the detection of voice disorders.
        in: ISCA Tutorial and Research Workshop (ITRW) on Non-Linear Speech Processing. 2005
        • Nayak J.
        • Bhat P.S.
        • Acharya R.
        • et al.
        Classification and analysis of speech abnormalities.
        ITBM-RBM. 2005; 26: 319-327
        • Moran R.J.
        • Reilly R.B.
        • de Chazal P.
        • et al.
        Telephony-based voice pathology assessment using automated speech analysis.
        IEEE Trans Biomed Eng. 2006; 53: 468-477
        • Schlotthauer G.
        • Torres M.E.
        • Jackson-Menaldi M.C.
        Automatic diagnosis of pathological voices.
        WSEAS Trans Signal Process. 2006; 2: 1260-1267
        • Fonseca E.S.
        • Guido R.C.
        • Scalassara P.R.
        • et al.
        Wavelet time- frequency analysis and least squares support vector machines for the identification of voice disorders.
        Comput Biol Med. 2007; 37: 571-578
        • Kukharchik P.
        • Martynov D.
        • Kheidorov I.
        • et al.
        Vocal fold pathology detection using modified wavelet-like features and support vector machines.
        in: 15th European on Signal Processing Conference. IEEE, 2007: 2214-2218
        • Aguiar Neto B.G.
        • Costa S.C.
        • Fechine J.M.
        LPC modelling and cepstral analysis applied to vocal fold pathology detection.
        Int J Funct Inf Personal Med. 2008; 1: 156-170
        • Linder R.
        • Albers A.E.
        • Hess M.
        • et al.
        Artificial neural network- based classification to screen for dysphonia using psychoacoustic scaling of acoustic voice features.
        J voice. 2008; 22: 155-163
        • Murugesapandian P.
        • Yaacob S.
        • Hariharan M.
        Feature extraction based on mel-scaled wavelet packet transform for the diagnosis of voice disorders.
        in: 4th Kuala Lumpur International Conference on Biomedical Engineering. Springer, Berlin, Heidelberg2008: 790-793
        • Salhi L.
        • Talbi M.
        • Cherif A.
        Voice disorders identification using hybrid approach: wavelet analysis and multilayer neural networks.
        World Acad Sci Eng Technol. 2008; 45: 330-339
        • Hariharan M.
        • Paulraj M.P.
        • Yaacob S.
        Identification of vocal fold pathology basedonmelfrequencyband energy coefficientsand singular valuedcomposition.
        in: 2009 IEEE International Conference on Signal and Image Processing Applications (ICSIPA). IEEE, 2009: 514-517
        • Kotropoulos C.
        • Arce G.R.
        Linear classifier with reject option for the detection of vocal fold paralysis and vocal fold edema.
        EURASIP J Adv Signal Process. 2009; 2009: 11
        • Markaki M.
        • Stylianou Y.
        Normalized modulation spectral features for cross-database voice pathology detection.
        in: Tenth Annual Conference of the International Speech Communication Association. 2009
        • Markaki M.
        • Stylianou Y.
        Using modulation spectra for voice pathology detection and classification.
        in: Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2009 (EMBC 2009). IEEE, 2009: 2514-2517
        • Markaki M.
        • Stylianou Y.
        • Arias-Londoño J.D.
        • et al.
        Dysphonia detection based on modulation spectral features and cepstral coefficients.
        in: 2010 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP). IEEE, 2010: 5162-5165
        • Carvalho R.T.S.
        • Cavalcante C.C.
        • Cortez P.C.
        Wavelet transform and artificial neural networks applied to voice disorders identification.
        in: 2011 Third World Congress on Nature and Biologically Inspired Computing (NaBIC). IEEE, 2011: 371-376
        • de Bruijn M.
        • ten Bosch L.
        • Kuik D.J.
        • et al.
        Artificial neural network analysis to assess hypernasality in patients treated for oral or oropharyngeal cancer.
        Logoped Phoniatr Vocol. 2011; 36: 168-174
        • Lee J.
        • Jeong S.
        • Hahn M.
        • et al.
        An efficient approach using HOS-based parameters in the LPC residual domain to classify breathy and rough voices.
        Biomed Signal Process Control. 2011; 6: 186-196
        • Tsanas A.
        • Little M.A.
        • McSharry P.E.
        • et al.
        Novel speech signal processing algorithms for high-accuracy classification of Parkinson's disease.
        IEEE Trans Biomed Eng. 2012; 59: 1264-1271
        • Ali Z.
        • Alsulaiman M.
        • Muhammad G.
        • et al.
        Vocal fold disorder detection based on continuous speech by using MFCC and GMM.
        in: 2013 7th IEEE GCC Conference and Exhibition (GCC). IEEE, 2013: 292-297
        • Alsulaiman M.
        • Muhammad G.
        • Ali Z.
        Classification of vocal fold diseases using RASTA-PLP.
        in: Proceedings of the International Conference on Bioinformatics & Computational Biology (BIOCOMP). The Steering Committee of the World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp), 2013: 1
        • Belalcazar-Bolanos E.A.
        • Orozco-Arroyave J.R.
        • Arias-Londono J.D.
        • et al.
        Automatic detection of Parkinson's disease using noise measures of speech.
        in: IEEE XVIII Symposium of Image, Signal Processing, and Artificial Vision (STSIVA). 2013: 1-5
        • Holi M.S.
        Automatic detection of neurological disordered voices using mel cepstral coefficients and neural networks.
        in: Point-of-Care Healthcare Technologies (PHT), 2013 IEEE. IEEE, 2013: 76-79
      2. Majidnezhad, V., & Kheidorov, I. An ANN-based method for detecting vocal fold pathology. arXiv preprint arXiv:1302.1772;2013.

        • Saldanha J.C.
        • Ananthakrishna T.
        • Pinto R.
        Vocal fold pathology assessment using PCA and LDA.
        in: Proceedings of 2013 International Conference on Intelligent Systems and Signal Processing (ISSP). IEEE, 2013: 140-144
        • Vikram C.M.
        • Umarani K.
        Phoneme independent pathological voice detection using wavelet based MFCCs, GMM-SVM hybrid classifier.
        in: Proceedings of 2013 International IEEE Conference on Advances in Computing, Communications and Informatics (ICACCI). IEEE, 2013: 929-934
        • Cordeiro H.
        • Fonseca J.
        • Meneses C.
        Spectral envelope and periodic component in classification trees for pathological voice diagnostic.
        in: IEEE 36th Annual International Conference on Engineering in Medicine and Biology Society (EMBC). 2014: 4607-4610
        • Fezari M.
        • Amara F.
        • El-Emary I.M.
        Acoustic analysis for detection of voice disorders using adaptive features and classifiers.
        in: Proceeeings of 2014th International Conference on Circuits, Systems and Control, Switzerland, February 22–24, 2014. 2014
        • El Emary I.M.M.
        • Fezari M.
        • Amara F.
        Towards developing a voice pathologies detection system.
        J Commun Technol Electron. 2014; 59: 1280-1288
        • Novotný M.
        • Rusz J.
        • Čmejla R.
        • et al.
        Automatic evaluation of articulatory disorders in Parkinson's disease.
        IEEE/ACM Trans Audio Speech Lang Process. 2014; 22: 1366-1378
        • Cordeiro H.
        • Fonseca J.
        • Guimarães I.
        • et al.
        Voice pathologies identification speech signals, features and classifiers evaluation.
        in: Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA). IEEE, 2015: 81-86
        • López-de-Ipina K.
        • Solé-Casals J.
        • Eguiraun H.
        • et al.
        Feature selection for spontaneous speech analysis to aid in Alzheimer's disease diagnosis: a fractal dimension approach.
        Comput Speech Lang. 2015; 30: 43-60
        • Rani K.U.
        • Holi M.S.
        GMM classifier for identification of neurological disordered voices using MFCC features.
        IOSR J VLSI Signal Process. 2015; 4: 44-51
        • Salehi P.
        Using patient's speech signal for vocal ford disorders detection based on lifting scheme.
        in: 2015 2nd International Conference on Knowledge-Based Engineering and Innovation (KBEI). IEEE, 2015: 561-568
        • Vásquez-Correa J.C.
        • Arias-Vergara T.
        • Orozco-Arroyave J.R.
        • et al.
        Automatic detection of Parkinson's disease from continuous speech recorded in non-controlled noise conditions.
        in: Proceedings of Sixteenth Annual Conference of the International Speech Communication Association. 2015
        • Agarwal A.
        • Chandrayan S.
        • Sahu S.S.
        Prediction of Parkinson's disease using speech signal with extreme learning machine.
        in: Proceedings of IEEE International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT). 2016: 3776-3779
        • Aicha A.B.
        • Ezzine K.
        Cancer larynx detection using glottal flow parameters and statistical tools.
        in: International Symposium on Signal, Image, Video and Communications (ISIVC). IEEE, 2016: 65-70
        • Francis C.R.
        • Nair V.V.
        • Radhika S.
        A scale invariant technique for detection of voice disorders using modified Mellin transform.
        in: IEEE International Conference on Emerging Technological Trends (ICETT). 2016: 1-6
        • Hammami I.
        • Salhi L.
        • Labidi S.
        Pathological voices detection using support vector machine.
        in: 2016 2nd International Conference on Advanced Technologies for Signal and Image Processing (ATSIP). IEEE, 2016: 662-666
        • Shahsavari M.K.
        • Rashidi H.
        • Bakhsh H.R.
        Efficient classification of Parkinson's disease using extreme learning machine and hybrid particle swarm optimization.
        in: IEEE 4th International Conference on Control, Instrumentation, and Automation (ICCIA). 2016: 148-154
        • Sharma R.K.
        • Gupta A.K.
        Processing and analysis of human voice for assessment of Parkinson disease.
        J Med Imaging Health Inf. 2016; 6: 63-70
        • Wang Z.
        • Yu P.
        • Yan N.
        • et al.
        Automatic assessment of pathological voice quality using multidimensional acoustic analysis based on the GRBAS scale.
        J Signal Process Syst. 2016; 82: 241-251
        • Benba A.
        • Jilbab A.
        • Hammouch A.
        Voice assessments for detecting patients with neurological diseases using PCA and NPCA.
        Int J Speech Technol. 2017; 20: 673-683
        • Dahmani M.
        • Guerti M.
        Vocal folds pathologies classification using Naïve Bayes Networks.
        in: 2017 6th International Conference on Systems and Control (ICSC). IEEE, 2017: 426-432
        • Muhammad G.
        • Alsulaiman M.
        • Ali Z.
        • et al.
        Voice pathology detection using interlaced derivative pattern on glottal source excitation.
        Biomed Signal Process Control. 2017; 31: 156-164
        • Shia S.E.
        • Jayasree T.
        Detection of pathological voices using discrete wavelet transform and artificial neural networks.
        in: 2017 IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS). IEEE, 2017, March: 1-6
        • Harar P.
        • Alonso-Hernandezy J.B.
        • Mekyska J.
        • et al.
        Voice pathology detection using deep learning: a preliminary study.
        in: IEEE International Conference and Workshop on Bioinspired Intelligence (IWOBI). 2017: 1-4