International Journal of Engineering and Manufacturing (IJEM)
IJEM Vol. 7, No. 2, 8 Mar. 2017
Cover page and Table of Contents: PDF (size: 385KB)
Voice Pathology Identification: A Survey on Voice Disorder
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Author(s)
Index Terms
Voice Pathology, Classification, Acoustic Analysis, Vocal Fold, MDVP Parameters
Abstract
Nowadays, Identification and Classification of voice pathology plays a major role in the field of speech processing. This paper explores and compares various things like input database, parameters, features extraction techniques, methodology and classification techniques used by the researchers in the problem of identifying the voice pathology. In this paper, we compared seven research works done in the field of voice pathology identification and classification. By analyzing the data's mentioned in these research papers and by considering these research papers as a base study, we wish to do the further research on voice pathology identification.
Cite This Paper
N.A. Sheela Selvakumari, V.Radha,"Voice Pathology Identification: A Survey on Voice Disorder", International Journal of Engineering and Manufacturing(IJEM), Vol.7, No.2, pp.39-49, 2017. DOI: 10.5815/ijem.2017.02.04
Reference
[1]Raissa Tavares , Nathalia Monteir, "Optimizing laryngeal pathology detection by using combined cepstral features'' Proceedings of 20th International Congress on acoustics, ICA 2010 23- 27 August 2010, Sydney, Australia ICA 2010.
[2]Julian D. Arias-Londono,"Complexity Analysis of Pathological Voices by means Markov Entropy measurements'', 31st Annual International Conference of the IEEE EMBS Minneapolis, Minnesota, USA, September 2-6, 2009.
[3]Darcio G. Silva, Luıs C. Oliveira and Mario Andrea "Jitter Estimation Algorithms for Detection of Pathological Voices" Hindawi Publishing Corporation, EURASIP Journal on Advances in Signal Processing Volume, Article ID 567875, 2009.
[4]Miltiadis Vasilakis, Yannis Stylianou "Voice Pathology Detection Based on Short-Term Jitter Estimations in Running Speech'' Folia Phoniatr Logop, PP. 153–170, 2009.
[5]Sonu, R. K. Sharma "Disease Detection Using Analysis of Voice Parameters'' International Journal of Computing Science and Communication Technologies, VOL.4 NO. 2, January 2012.
[6]Jacques Koremana, "Correlates of Varying Vocal Fold Adduction Deficiencies in Perception and Production: Methodological and Practical Considerations" Folia Phoniatr Logop, PP: 305–320, 2004.
[7]Alireza A. Dibazar, Theodore W. Berger, and Shrikanth S. Narayanan "Pathological Voice Assessment" IEEE EMBS, NEW YORK, 2006.
[8]J. H. L. Hansen et al., "A Non Linear operator based speech feature analysis method with application to vocal fold pathology assessment," IEEE Trans. Biomed. Eng., 1995.
[9]L. Salhi, T. Mourad, and A. Cherif, "Voice Disorders Identification using Multilayer Neural Network," Int. Arab. J. Inf. Technol., Vol. 7, PP. 177–185, 2010.
[10]N. Saenz Lechon, et al., "Methodological issues in the development of automatic systems for voice pathology detection," Biomed. Signal Processing Control 1, PP. 120– 128, 2006.
[11]J. I. Godino Llorente, "Support Vector Machines Applied to the Detection of Voice Disorders", Springer Verlag, Berlin, PP. 219–230, 2005.
[12]Zulfiqar Ali, Irraivan Elamvazuthi, Mansour Alsulaiman, Ghulam Muhammad, "Detection of Voice Pathology using Fractal Dimension in a Multiresolution Analysis of Normal and Disordered Speech Signals", Journal of Medical Systems, Springer, 2015.
[13]Zulfiqar Ali, Irraivan Elamvazuthi, Mansour Alsulaiman, Ghulam Muhammad, "Automatic Voice Pathology Detection with Running Speech by Using Estimation of Auditory Spectrum and Cepstral Coefficients Based on the All-Pole Model", Journal of Voice, Elsevier, 2015.
[14]Zulfiqar Ali, Mansour Alsulaiman, Irraivan Elamvazuthi, Ghulam Muhammad,"Voice Pathology Detection based on the Modified Voice Contour and SVM", Biologically Inspired Cognitive Architectures (BICA), Elsevier, 2015.
[15]Zhijian Wang, Ping Yu, Nan Yan, Lan Wang, Manwa L. Ng, "Automatic Assessment of Pathological Voice Quality Using Multidimensional Acoustic Analysis Based on the GRBAS Scale. Signal Processing Systems 82(2), PP: 241-251, 2016.
[16]Ping Yu, Zhijian Wang, Shanshan Liu, Nan Yan, Lan Wang, Manwa L. Ng, "Multidimensional acoustic analysis for voice quality assessment based on the GRBAS scale", ISCSLP, PP: 321-325, 2014.
[17]Pouria Saidi, Farshad Almasganj, "Voice Disorder Signal Classification Using M-Band Wavelets and Support Vector Machine", CSSP 34(8), PP: 2727-2738, 2015.
[18]Nafise Erfanian Saeedi, Farshad Almasganj, Farhad Torabinejad, "Support vector wavelet adaptation for pathological voice assessment", Comp. in Bio. and Med. 41(9), PP: 822-828, 2011.
[19]Nafise Erfanian Saeedi, Farshad Almasganj, "Wavelet adaptation for automatic voice disorders sorting". Comp. in Bio. and Med. 43(6), PP: 699-704, 2013.
[20]Evaldas Vaiciukynas, Marija Bacauskiene, Virgilijus Uloza, "Exploring similarity-based classification of larynx disorders from human voice", Speech Communication, 54(5), PP: 601-610, 2012.
[21]Evaldas Vaiciukynas, Antanas Verikas, Adas, "Fusing voice and query data for non-invasive detection of laryngeal disorders". Expert Syst. Appl. 42(22), PP:8445-8453, 2015.
[22]Adas Gelzinis, Evaldas Padervinskis, "Exploring sustained phonation recorded with acoustic and contact microphones to screen for laryngeal disorders". CICARE , PP: 125-132, 2014.
[23]Karmele Lopez de Ipina, Harkaitz Egiraun, "Feature Extraction Approach Based on Fractal Dimension for Spontaneous Speech Modelling Oriented to Alzheimer Disease Diagnosis". NOLISP , PP: 144-151, 2013.
[24]Karmele Lopez de Ipina, Blanca Beitia, "Feature selection for spontaneous speech analysis to aid in Alzheimer's disease diagnosis: A fractal dimension approach", Computer Speech & Language 30(1), PP: 43-60, 2015.
[25]Karmele Lopez de Ipina, Harkaitz Eguiraun", On Automatic Diagnosis of Alzheimer's Disease Based on Spontaneous Speech Analysis and Emotional Temperature", Cognitive Computation 7(1) PP: 44-55, 2015.
[26]Fethi AMARA, Mohamed FEZARI, Hocine BOUROUBA, "An Improved GMM-SVM System based on Distance Metric for Voice Pathology Detection", Applied Mathematics & Information Sciences, May 2016.
[27]M. M. El Emary, M. Fezari , F. Amara, "Towards developing a voice pathologies detection system", Journal of Communications Technology and Electronics, Nov 2014.