Research article Special Issues

Meta-analysis of voice disorders databases and applied machine learning techniques

  • Received: 20 August 2020 Accepted: 25 October 2020 Published: 11 November 2020
  • Background and ObjectiveVoice disorders are pathological conditions that directly affect voice production. Computer based diagnosis may play a major role in the early detection and in tracking and even development of efficient pathological speech diagnosis, based on a computerized acoustic evaluation. The health of the Voice is assessed by several acoustic parameters. The exactness of these parameters is often linked to algorithms used to estimate them for speech noise identification. That is why main effort of the scientists is to study acoustic parameters and to apply classification methods that achieve a high precision in discrimination. The primary aim of this paper is for a meta-analysis on voice disorder databases i.e. SVD, MEEI and AVPD and machine learning techniques applied on it. Materials and MethodsThis field of study was systematically reviewed in compliance with PRISMA guidelines. A search was performed with a set of formulated keywords on three databases i.e. Science Direct, PubMed, and IEEE Xplore. A proper screening and analysis of articles were performed after which several articles were also excluded. ResultsForty-five studies that fulfills the eligibility criteria were included in this meta-analysis. After applying eligibility criteria on the peer reviewed and research article and studies that were published in authentic journals and conferences proceedings till June 2020 were chosen for further full-text screening. In general, only those articles that used voice recordings from SVD, MEEI and AVPD databases as a dataset is included in this meta-analysis. ConclusionWe discussed the strengths and weaknesses of SVD, MEEI and AVPD. After detailed analysis of the studies including the techniques used and outcome measurements, it was also concluded that Support Vector Machine (SVM) is the most common used algorithm for the detection of voice disorders. Other than was also noticed that researchers focus on supervised techniques for the clinical diagnosis of voice disorder rather than using unsupervised techniques. It was also concluded that more work needs to be on voice pathology detection using AVPD database.

    Citation: Sidra Abid Syed, Munaf Rashid, Samreen Hussain. Meta-analysis of voice disorders databases and applied machine learning techniques[J]. Mathematical Biosciences and Engineering, 2020, 17(6): 7958-7979. doi: 10.3934/mbe.2020404

    Related Papers:

  • Background and ObjectiveVoice disorders are pathological conditions that directly affect voice production. Computer based diagnosis may play a major role in the early detection and in tracking and even development of efficient pathological speech diagnosis, based on a computerized acoustic evaluation. The health of the Voice is assessed by several acoustic parameters. The exactness of these parameters is often linked to algorithms used to estimate them for speech noise identification. That is why main effort of the scientists is to study acoustic parameters and to apply classification methods that achieve a high precision in discrimination. The primary aim of this paper is for a meta-analysis on voice disorder databases i.e. SVD, MEEI and AVPD and machine learning techniques applied on it. Materials and MethodsThis field of study was systematically reviewed in compliance with PRISMA guidelines. A search was performed with a set of formulated keywords on three databases i.e. Science Direct, PubMed, and IEEE Xplore. A proper screening and analysis of articles were performed after which several articles were also excluded. ResultsForty-five studies that fulfills the eligibility criteria were included in this meta-analysis. After applying eligibility criteria on the peer reviewed and research article and studies that were published in authentic journals and conferences proceedings till June 2020 were chosen for further full-text screening. In general, only those articles that used voice recordings from SVD, MEEI and AVPD databases as a dataset is included in this meta-analysis. ConclusionWe discussed the strengths and weaknesses of SVD, MEEI and AVPD. After detailed analysis of the studies including the techniques used and outcome measurements, it was also concluded that Support Vector Machine (SVM) is the most common used algorithm for the detection of voice disorders. Other than was also noticed that researchers focus on supervised techniques for the clinical diagnosis of voice disorder rather than using unsupervised techniques. It was also concluded that more work needs to be on voice pathology detection using AVPD database.


    加载中


    [1] S. Misono, S. Marmor, N. Roy, T. Mau, S. Cohen, Multi-institutional study of voice disorders and voice therapy referral, Otolaryngol. Head Neck Surgery, 155 (2016), 33-41. doi: 10.1177/0194599816639244
    [2] P. Bradley, Voice disorders: Classification, Otolaryngol. Head Neck Surgery, (2010), 555-562.
    [3] M. Behlau, M. L. S. Dragone, L. Nagano, The voice that teaches: The teacher and oral communication in the classroom, 2004.
    [4] A. E. Aronson, Clinical voice disorders, 3 ed., INC. New York: Thieme Medical Publishers, 1990, p. 3-11.
    [5] J. R. Spiegel, R. T. Sataloff, K. A. Emerich, The young adult voice, J. Voice, 11 (1997), 138-143. doi: 10.1016/S0892-1997(97)80069-0
    [6] L. O. Ramig, K. Verdolini, Treatment efficacy: Voice disorders, J. Speech Lang. Hear. Res., 41 (1998), 101-106.
    [7] J. Baker, The role of psychogenic and psychosocial factors in the development of functional voice disorders, J. Speech Lang. Pathol., 10 (2008), 210-230. doi: 10.1080/17549500701879661
    [8] S. T. Kasama, A. G. Brasolotto, Vocal perception and life quality, Pro. Fono., 9 (2007), 19-28.
    [9] L. P. Ferreira, J. G. Santos, M. F. B. Lima, Vocal sympton and its probable cause: Data colleting in a population, Rev. CEFAC, 11 (2009), 110-118. doi: 10.1590/S1516-18462009000100015
    [10] P. H. Dejonckere, P. Bradley, P. Clemente, G. Cornut G, L. C. Buchman, G. Friedrich, 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., 258 (2001), 77-82. doi: 10.1007/s004050000299
    [11] U. Cesari, G. De Pietro, E. Marciano, C. Niri, G. Sannino, L. Verde, Voice disorder detection via an m-Health system: Design and results of a clinical study to evaluate Vox4Health, BioMed. Res. Int., 2018 (2018), 1-19.
    [12] L. Verde, G. De Pietro, G. Sannino, Voice disorder identification by using machine learning techniques, IEEE Access, 6 (2018), 16246-16255. doi: 10.1109/ACCESS.2018.2816338
    [13] A. G. David, J. B. Magnus, Diagnosing parkinson by using artificial neural networks and support vector machines, Global J. Comput. Sci. Technol., (2009), 63-71.
    [14] Saarbruecken Voice Database—Handbook, Stimmdatenbank.coli.uni-saarland.de. [Online]. Available: http://www.stimmdatenbank.coli.uni-saarland.de/help_en.php4.
    [15] M. OpenCourseWare, Lab Database | Laboratory on the Physiology, Acoustics, and Perception of Speech | Electrical Engineering and Computer Science | MIT OpenCourseWare, Ocw.mit.edu. [Online]. Available: https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-542j-laboratory-on-the-physiology-acoustics-and-perception-of-speech-fall-2005/lab-database/
    [16] K. Daoudi, B. Bertrac, On classification between normal and pathological voices using the MEEI-KayPENTAX database: Issues and consequences, INTERSPEECH-2014, Sep 2014, Singapour, Singapore. ffhal-01010857
    [17] N. Sáenz-Lechón, J. I. Godino-Llorente, V. Osma-Ruiz, P. Gómez-Vilda, Methodological issues in the development of automatic systems for voice pathology detection, Biomed. Signal Process. Control, 1 (2006), 120-128.
    [18] A. Liberati, D. G. Altman, J. Tetzlaff, C. Mulrow, P. C. Gøtzsche, J. P. A. Ioannidis, et al., The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate healthcare interventions: Explanation and elaboration, BMJ, 339 (2009).
    [19] A. Al-Nasheri, G. Muhammad, M. Alsulaiman, Z. Ali, K. H. Malki, T. A. Mesallam, et al., Voice pathology detection and classification using auto-correlation and entropy features in different frequency regions, IEEE Access, 6, 6961-6974.
    [20] A. Al-Nasheri, G. Muhammad, M. Alsulaiman, Z. Ali, T. A. Mesallam, M. Farahat, et al., An investigation of multidimensional voice program parameters in three different databases for voice pathology detection and classification, J. Voice, 31 (2017), 113.e9-e18.
    [21] A. Al-Nasheri, G. Muhammad, M. Alsulaiman, Z. Ali, Investigation of voice pathology detection and classification on different frequency regions using correlation functions, J. Voice, 31 (2017), 3-15. doi: 10.1016/j.jvoice.2016.01.014
    [22] Z. Ali, M. Alsulaiman, G. Muhammad, I. Elamvazuthi, A. Al-Nasheri, T. A. Mesallam, K. H. Malki, et al., Intra- and inter-database study for Arabic, English, and German databases: Do conventional speech features detect voice pathology?, J. Voice, 31 (2017), 386.e1-e8.
    [23] E. S. Fonseca, R. C. Guido, S. B. Junior, H. Dezani, R. R. Gati, D. C. Mosconi Pereira, Acoustic investigation of speech pathologies based on the discriminative paraconsistent machine (DPM), Biomed. Signal Process. Control, 55 (2020).
    [24] J. A. Gómez-García, L. Moro-Velázquez, J. Mendes-Laureano, G. Castellanos-Dominguez, J. I. Godino-Llorente, Emulating the perceptual capabilities of a human evaluator to map the GRB scale for the assessment of voice disorders, Eng. Appl. Artific. Intell., 82 (2019), 236--251. doi: 10.1016/j.engappai.2019.03.027
    [25] V. Guedes, F. Teixeira, A. Oliveira, J. Fernandes, L. Silva, A. Junior, et al., Transfer Learning with AudioSet to Voice Pathologies Identification in Continuous Speech, Proced. Comput. Sci., 164 (2019), 662-669. doi: 10.1016/j.procs.2019.12.233
    [26] I. Hammami, L. Salhi, S. Labidi, Voice pathologies classification and detection using EMD- DWT analysis based on higher order statistic features, IRBM, 41 (2020), 161-171. doi: 10.1016/j.irbm.2019.11.004
    [27] D. Hemmerling, A. Skalski, J. Gajda, Voice data mining for laryngeal pathology assessment, Comput. Biol. Med., 69 (2016), 270-276. doi: 10.1016/j.compbiomed.2015.07.026
    [28] J. Moon, S. Kim, An approach on a combination of higher-order statistics and higher-order differential energy operator for detecting pathological voice with machine learning, 2018 International Conference on Information and Communication Technology Convergence (ICTC), 17-19 Oct. 2018, pp. 46-51.
    [29] K. Ezzine, M. Frikha, Investigation of glottal flow parameters for voice pathology detection on SVD and MEEI databases, 2018 4th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP), 21-24 March 2018, pp. 1-6.
    [30] M. Markaki, Y. Stylianou, Using modulation spectra for voice pathology detection and classification, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 3-6 Sept. 2009, pp. 2514-2517.
    [31] M. Markaki, Y. Stylianou, Voice pathology detection and discrimination based on modulation spectral features, IEEE Transact. Aud. Speech Langu. Process, 19 (2011), 1938-1948. doi: 10.1109/TASL.2010.2104141
    [32] J. M. Miramont, J. F. Restrepo, J. Codino, C. Jackson-Menaldi, G. Schlotthauer, Voice signal typing using a pattern recognition approach, J. Voice, 2020.
    [33] G. Muhammad, M. Alsulaiman, Z. Ali, T. A. Mesallam, M. Farahat, K. H. Malki, et al., Voice pathology detection using interlaced derivative pattern on glottal source excitation, Biomed. Signal Process. Control, 31 (2017), 156-164. doi: 10.1016/j.bspc.2016.08.002
    [34] S. E. Shia, T. Jayasree, Detection of pathological voices using discrete wavelet transform and artificial neural networks, 2017 IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS), 23-25 March 2017, pp. 1-6.
    [35] S. R. Kadiri, P. Alku, Analysis and detection of pathological voice using glottal source features, IEEE J. Select. Topics Signal Process., 14 (2020), 367-379. doi: 10.1109/JSTSP.2019.2957988
    [36] T. Zhang, Y. Shao, Y. Wu, Z. Pang, G. Liu, Multiple vowels repair based on pitch extraction and line spectrum pair feature for voice disorder, IEEE J. Biomed. Health Inform., 24 (2020), 1940-1951. doi: 10.1109/JBHI.2020.2978103
    [37] F. Teixeira, J. Fernandes, V. Guedes, A. Junior, J. P. Teixeira, Classification of control/pathologic subjects with support vector machines, Proced. Comput. Sci., 138 (2018), 272-279. doi: 10.1016/j.procs.2018.10.039
    [38] J. P. Teixeira, P. O. Fernandes, N. Alves, Vocal acoustic analysis—classification of dysphonic voices with artificial neural networks, Proced. Comput. Sci., 121 (2017), 19-26. doi: 10.1016/j.procs.2017.11.004
    [39] G. Muhammad, M. Melhem, Pathological voice detection and binary classification using MPEG-7 audio features, Biomed. Signal Process. Control, 11 (2014), 1-9. doi: 10.1016/j.bspc.2014.02.001
    [40] J. Nayak, P. S. Bhat, R. Acharya, U. V. Aithal, Classification and analysis of speech abnormalities, ITBM-RBM, 26 (2005), 319-327. doi: 10.1016/j.rbmret.2005.05.002
    [41] Z. Ali, I. Elamvazuthi, M. Alsulaiman, G. Muhammad, Automatic voice pathology detection with running speech by using estimation of auditory spectrum and cepstral coefficients based on the all-pole model, J. Voice, 30 (2016), 757.e7-e19.
    [42] R. Amami, A. Smiti, An incremental method combining density clustering and support vector machines for voice pathology detection, Comput. Electr. Eng., 57 (2017), 257-265. doi: 10.1016/j.compeleceng.2016.08.021
    [43] J. D. Arias-Londoño, J. I. Godino-Llorente, N. Sáenz-Lechón, V. Osma-Ruiz, G. Castellanos- Domínguez, An improved method for voice pathology detection by means of a HMM-based feature space transformation, Patt. Recogn., 43 (2010), 3100-3112.
    [44] M. K. Arjmandi, M. Pooyan, M. Mikaili, M. Vali, A. Moqarehzadeh, Identification of voice disorders using long-time features and support vector machine with different feature reduction methods, J. Voice, 25 (2011), e275-e289. doi: 10.1016/j.jvoice.2010.08.003
    [45] R. R. A. Barreira, L. L. Ling, Kullback-leibler divergence and sample skewness for pathological voice quality assessment, Biomed. Signal Process. Control, 57 (2020), 101697. doi: 10.1016/j.bspc.2019.101697
    [46] C. R. Francis, V. V. Nair, S. Radhika, A scale invariant technique for detection of voice disorders using Modified Mellin Transform, 2016 International Conference on Emerging Technological Trends (ICETT), 21-22 Oct. 2016, pp. 1-6.
    [47] H. Cordeiro, J. Fonseca, I. Guimarães, C. Meneses, Hierarchical classification and system combination for automatically identifying physiological and neuromuscular laryngeal pathologies, J. Voice, 31 (2017), 384.
    [48] H. T. Cordeiro, C. M. Ribeiro, Spectral envelope first peak and periodic component in pathological voices: A spectral analysis, Proced. Comput. Sci., 138 (2018), 64-71. doi: 10.1016/j.procs.2018.10.010
    [49] S. H. Fang, Y. Tsao, M. J. Hsiao, J. Y. Chen, Y. H. Lai, F. C. Lin, et al., Detection of pathological voice using cepstrum vectors: A deep learning approach, J. Voice, 33 (2019), 634-641. doi: 10.1016/j.jvoice.2018.02.003
    [50] G. Muhammad, Voice pathology detection using vocal tract area, 2013 European Modelling Symposium, 20-22 Nov. 2013, pp. 164-168.
    [51] H. Ghasemzadeh, M. Tajik Khass, M. Khalil Arjmandi, M. Pooyan, Detection of vocal disorders based on phase space parameters and Lyapunov spectrum, Biomed. Signal Process. Control, 22 (2015), 135-145. doi: 10.1016/j.bspc.2015.07.002
    [52] J. I. Godino-Llorente, R. Fraile, N. Sáenz-Lechón, V. Osma-Ruiz, P. Gómez-Vilda, Automatic detection of voice impairments from text-dependent running speech, Biomed. Signal Process. Control, 4 (2009), 176-182.
    [53] M. Hariharan, K. Polat, R. Sindhu, S. Yaacob, A hybrid expert system approach for telemonitoring of vocal fold pathology, Appl. Soft Comput., 13 (2013), 4148-4161. doi: 10.1016/j.asoc.2013.06.004
    [54] A. Mahmood, A solution to the security authentication problem in smart houses based on speech, Proced. Comput. Sci., 155 (2019), 606-611. doi: 10.1016/j.procs.2019.08.085
    [55] J. Mekyska, E. Janousova, P. Gomez-Vilda, Z. Smekal, I. Rektorova, I. Eliasova, et al., Robust and complex approach of pathological speech signal analysis, Neurocomputing, 167 (2015), 94-111. doi: 10.1016/j.neucom.2015.02.085
    [56] G. Muhammad, M. Melhem, Pathological voice detection and binary classification using MPEG-7 audio features, Biomed. Signal Process. Control, 11 (2014), 1-9. doi: 10.1016/j.bspc.2014.02.001
    [57] J. Nayak, P. S. Bhat, R. Acharya, U. V. Aithal, Classification and analysis of speech abnormalities, ITBM-RBM, 26 (2005), 319-327. doi: 10.1016/j.rbmret.2005.05.002
    [58] P. Henriquez, J. B. Alonso, M. A. Ferrer, C. M. Travieso, J. I. Godino-Llorente, F. Diaz-de- Maria, Characterization of healthy and pathological voice through measures based on nonlinear dynamics, IEEE Transact. Audio Speech Lang. Process., 17 (2009), 1186-1195.
    [59] P. Salehi, 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), 5-6 Nov. 2015, pp. 561-568.
    [60] N. Sáenz-Lechón, J. I. Godino-Llorente, V. Osma-Ruiz, P. Gómez-Vilda, Methodological issues in the development of automatic systems for voice pathology detection, Biomed. Signal Process. Control, 1 (2006), 120-128.
    [61] C. M. Travieso, J. B. Alonso, J. R. Orozco-Arroyave, J. F. Vargas-Bonilla, E. Nöth, A. G. Ravelo- García, Detection of different voice diseases based on the nonlinear characterization of speech signals, Expert Systems Appl., 82 (2017), 184-195.
    [62] T. A. Mesallam, F. Mohamed, K. H. Malki, A. Mansour, A. Zulfiqar, A. N. Ahmed, et al., Development of the arabic voice pathology database and its evaluation by using speech features and machine learning algorithms, J. Healthc. Eng., (2017), 1-13.
    [63] K. Uma Rani, Mallikarjun S Holi, A comparative study of neural networks and support vector machines for neurological disordered voice classification, Inter. J. Eng. Res. Techol., 3 (2014).
    [64] J. Godino-Llorente, P. Gómez-Vilda, N. Sáenz-Lechón, M. Blanco-Velasco, F. Cruz-Roldán, M. Ferrer-Ballester, Support vector machines applied to the detection of voice disorders, Nonlin. Analy. Algor. Speech Process., (2006), 219-230.
    [65] S. Huang, N. Cai, P. P. Pacheco, S. Narrandes, Y. Wang, W. Xu, Applications of support vector machine (svm) learning in cancer genomics, Cancer Genom. Proteom., 15 (2018).
    [66] S. Yue, P. Li, P. Hao, SVM classification: Its contents and challenges, Appl. Math. J. Chinese Univer., 18 (2003), 332-342. doi: 10.1007/s11766-003-0059-5
    [67] D. Reynolds, Gaussian Mixture Models, In: S. Z. Li, A. Jain (eds), Encyclopedia of Biometrics, Springer, Boston, MA, 2009.
    [68] L. Breiman, J. Friedman, C. J. Stone, R. A. Olshen, Classification and regression trees, Boca Raton, FL: CRC press, 1984.
    [69] L. Breiman, Bagging predictors, Mach. Learn., 24 (1996), 123-140.
    [70] S. Indolia, A. Goswami, S. Mishra, P. Asopa, Conceptual understanding of convolutional neural network- A deep learning approach, Proced. Computer Sci., 132 (2018), 679-688. doi: 10.1016/j.procs.2018.05.069
    [71] R. Yamashita, M. Nishio, R. Do, K. Togashi, Convolutional neural networks: An overview and application in radiology, Insights Imag., 9 (2018), 611-629. doi: 10.1007/s13244-018-0639-9
    [72] V. Parsa, D. G. Jamieson, Identification of pathological voices using glottal noise measures, J. Speech Langu. Hear. Res., 43 (2000), 469-485. doi: 10.1044/jslhr.4302.469
    [73] D. D. Deliyski, H. S. Shaw, M. K. Evans, Influence of sampling rate on accuracy and reliability of acoustic voice analysis, Logoped. Phoniatr. Vocol., 30 (2005), 55-62. doi: 10.1080/1401543051006721
    [74] Y. Horii, Jitter and shimmer in sustained vocal fry phonation, Folia Phoniatr., 37 (1985), 81-86. doi: 10.1159/000265785
    [75] J. L. Fitch, Consistency of fundamental frequency and perturbation in repeated phonations of sustained vowels, reading, and connected speech, J. Speech Hear. Disord., 55 (1990), 360-363. doi: 10.1044/jshd.5502.360
    [76] T. Mesallam, M. Farahat, K. Malki, M. Alsulaiman, Z. Ali, A. Al-nasheri, et al., Development of the arabic voice pathology database and its evaluation by using speech features and machine learning algorithms, J. Healthc. Eng., 2017, 1-13.
    [77] P. Harar, Z. Galaz, J. Alonso-Hernandez, J. Mekyska, R. Burget, Z. Smekal, Towards robust voice pathology detection, Neural Comput. Appl., 2018.
    [78] D. D. Mehta, R. E. Hillman, Voice assessment: Updates on perceptual, acoustic, aerodynamic, and endoscopic imaging methods, Curr. Opin. Otolaryngol. Head Neck Surg., 16 (2008), 211. doi: 10.1097/MOO.0b013e3282fe96ce
  • Reader Comments
  • © 2020 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(829) PDF downloads(95) Cited by(2)

Article outline

Figures and Tables

Figures(9)  /  Tables(2)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog