Towards robust voice pathology detection

Investigation of supervised deep learning, gradient boosting, and anomaly detection approaches across four databases


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 three 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 four 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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Fig. 1

Change history

  • 14 September 2019

    The Table 3 was published incorrectly in the original publication of the article.


  1. 1.

    Ali Z, Alsulaiman M, Muhammad G, Elamvazuthi I, Al-nasheri A, Mesallam TA, Farahat M, Malki KH (2017) Intra-and inter-database study for arabic, english, and german databases: do conventional speech features detect voice pathology? J Voice 31(3):e381–e386

    Article  Google Scholar 

  2. 2.

    Ali Z, Muhammad G, Alhamid MF (2017) An automatic health monitoring system for patients suffering from voice complications in smart cities. IEEE Access 5:3900–3908

    Article  Google Scholar 

  3. 3.

    Al-nasheri A, Muhammad G, Alsulaiman M, Ali Z (2017) Investigation of voice pathology detection and classification on different frequency regions using correlation functions. J Voice 31(1):3–15

    Article  Google Scholar 

  4. 4.

    Al-nasheri A, Muhammad G, Alsulaiman M, Ali Z, Malki K, Mesallam T, Farahat M (2017) Voice pathology detection and classification using auto-correlation and entropy features in different frequency regions. IEEE Access PP(99):1–1.

    Article  Google Scholar 

  5. 5.

    Al-nasheri A, Muhammad G, Alsulaiman M, Ali Z, Mesallam TA, Farahat M, Malki KH, Bencherif MA (2017) An investigation of multidimensional voice program parameters in three different databases for voice pathology detection and classification. J Voice 31(1):113–e119

    Article  Google Scholar 

  6. 6.

    Al-nasheri A, Ali Z, Muhammad G, Alsulaiman M (2014) Voice pathology detection using auto-correlation of different filters bank. In: 2014 IEEE/ACS 11th international conference on computer systems and applications (AICCSA), pp 50–55. IEEE

  7. 7.

    Amami R, Smiti A (2017) An incremental method combining density clustering and support vector machines for voice pathology detection. Comput Electr Eng 57:257–265

    Article  Google Scholar 

  8. 8.

    Arias-Londoño JD, Godino-Llorente JI, Markaki M, Stylianou Y (2011) On combining information from modulation spectra and mel-frequency cepstral coefficients for automatic detection of pathological voices. Logop Phoniatr Vocol 36(2):60–69

    Article  Google Scholar 

  9. 9.

    Armstrong D, Gosling A, Weinman J, Marteau T (1997) The place of inter-rater reliability in qualitative research: an empirical study. Sociology 31(3):597–606

    Article  Google Scholar 

  10. 10.

    Brabenec L, Mekyska J, Galaz Z, Rektorova I (2017) Speech disorders in parkinsons disease: early diagnostics and effects of medication and brain stimulation. J Neural Transm 124(3):303–334

    Article  Google Scholar 

  11. 11.

    Breiman L (2001) Random forests. Mach Learn 45(1):5–32

    MATH  Article  Google Scholar 

  12. 12.

    Chen T, Guestrin C (2016) Xgboost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp 785–794. ACM

  13. 13.

    Chollet F et al (2015) Keras: Deep learning library for theano and tensorflow.

  14. 14.

    Dahmani M, Guerti M (2017) Vocal folds pathologies classification using naïve bayes networks. In: 2017 6th international conference on systems and control (ICSC), pp 426–432. IEEE

  15. 15.

    De Bodt MS, Wuyts FL, Van de Heyning PH, Croux C (1997) Test–retest study of the grbas scale: influence of experience and professional background on perceptual rating of voice quality. J Voice 11(1):74–80

    Article  Google Scholar 

  16. 16.

    Dejonckere PH, Bradley P, Clemente P, Cornut G, Crevier-Buchman L, Friedrich G, Van De Heyning P, Remacle M, Woisard V (2001) 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(2):77–82

    Article  Google Scholar 

  17. 17.

    Eskidere Ö, Gürhanlı A (2015) Voice disorder classification based on multitaper mel frequency cepstral coefficients features. Comput Math Methods Med.

    Article  Google Scholar 

  18. 18.

    Eye M, Infirmary E (1994) Voice disorders database, version. 1.03 (cd-rom). Kay Elemetrics Corporation, Lincoln Park

  19. 19.

    Gerratt BR, Kreiman J, Antonanzas-Barroso N, Berke GS (1993) Comparing internal and external standards in voice quality judgments. J Speech Hear Res 36(1):14–20

    Article  Google Scholar 

  20. 20.

    Godino-Llorente JI, Gómez-Vilda P, Cruz-Roldán F, Blanco-Velasco M, Fraile R (2010) Pathological likelihood index as a measurement of the degree of voice normality and perceived hoarseness. J Voice 24(6):667–677

    Article  Google Scholar 

  21. 21.

    Gwet KL (2014) Handbook of inter-rater reliability: the definitive guide to measuring the extent of agreement among raters. Advanced Analytics LLC, Montgomery

    Google Scholar 

  22. 22.

    Harar P, Alonso-Hernandezy JB, Mekyska J, Galaz Z, Burget R, Smekal Z (2017) Voice pathology detection using deep learning: a preliminary study. In: 2017 international conference and workshop on bioinspired intelligence (IWOBI), pp 1–4. IEEE

  23. 23.

    Hartigan JA, Wong MA (1979) Algorithm as 136: a k-means clustering algorithm. J Roy Stat Soc Ser C (Appl Stat) 28(1):100–108

    MATH  Google Scholar 

  24. 24.

    Hearst MA, Dumais ST, Osuna E, Platt J, Scholkopf B (1998) Support vector machines. IEEE Intell Syst Their Appl 13(4):18–28

    Article  Google Scholar 

  25. 25.

    Hemmerling D (2017) Voice pathology distinction using autoassociative neural networks. In: 2017 25th European signal processing conference (EUSIPCO), pp 1844–1847. IEEE

  26. 26.

    Hemmerling D, Skalski A, Gajda J (2016) Voice data mining for laryngeal pathology assessment. Comput Biol Med 69:270–276

    Article  Google Scholar 

  27. 27.

    Hillenbrand J, Houde RA (1996) Acoustic correlates of breathy vocal quality: dysphonic voices and continuous speech. J Speech Hear Res 39(2):311–321

    Article  Google Scholar 

  28. 28.

    Hossain MS, Muhammad G (2016) Healthcare big data voice pathology assessment framework. IEEE Access 4:7806–7815

    Article  Google Scholar 

  29. 29.

    Huang GB, Zhu QY, Siew CK (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1):489–501

    Article  Google Scholar 

  30. 30.

    Huang G, Liu Z, Weinberger KQ, van der Maaten L (2016) Densely connected convolutional networks. arXiv preprint arXiv:1608.06993

  31. 31.

    Kingma D, Ba J (2014) Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980

  32. 32.

    Kreiman J, Gerratt BR, Kempster GB, Erman A, Berke GS (1993) Perceptual evaluation of voice quality: review, tutorial, and a framework for future research. J Speech Hear Res 36(1):21–40

    Article  Google Scholar 

  33. 33.

    Little M, McSharry P, Hunter E, Spielman J, Ramig L (2009) Suitability of dysphonia measurements for telemonitoring of Parkinson’s disease. IEEE T Bio-Med Eng 56(4):1015–1022

    Article  Google Scholar 

  34. 34.

    Liu FT, Ting KM, Zhou ZH (2008) Isolation forest. In: Eighth IEEE international conference on data mining, 2008. ICDM’08, pp 413–422. IEEE

  35. 35.

    Liu FT, Ting KM, Zhou ZH (2012) Isolation-based anomaly detection. ACM Trans Knowl Discov Data (TKDD) 6(1):3

    Google Scholar 

  36. 36.

    Martínez D, Lleida E, Ortega A, Miguel A, Villalba J (2012) Voice pathology detection on the saarbrücken voice database with calibration and fusion of scores using multifocal toolkit. In: Advances in speech and language technologies for Iberian Languages, pp 99–109. Springer

  37. 37.

    Mehta DD, Hillman RE (2008) Voice assessment: updates on perceptual, acoustic, aerodynamic, and endoscopic imaging methods. Curr Opin Otolaryngol Head Neck Surg 16(3):211

    Article  Google Scholar 

  38. 38.

    Mekyska J, Janousova E, Gomez-Vilda P, Smekal Z, Rektorova I, Eliasova I, Kostalova M, Mrackova M, Alonso-Hernandez JB, Faundez-Zanuy M et al (2015) Robust and complex approach of pathological speech signal analysis. Neurocomputing 167:94–111

    Article  Google Scholar 

  39. 39.

    Mekyska J, Galaz Z, Mzourek Z, Smekal Z, Rektorova I (2015) Assessing progress of Parkinson’s using acoustic analysis of phonation. In: 2015 International work conference on bioinspired intelligence (IWOBI), pp 115–122.

  40. 40.

    Mekyska J, Smekal Z, Galaz Z, Mzourek Z, Rektorova I, Faundez-Zanuy M, López-de Ipiña K (2016) Recent advances in nonlinear speech processing, chap. Perceptual features as markers of Parkinson’s disease: the issue of clinical interpretability, pp 83–91. Springer, Cham.

  41. 41.

    Mesallam TA, Farahat M, Malki KH, Alsulaiman M, Ali Z, Al-nasheri A, Muhammad G (2017) Development of the Arabic voice pathology database and its evaluation by using speech features and machine learning algorithms. J Healthc Eng.

    Article  Google Scholar 

  42. 42.

    Michaelis D, Gramss T, Strube HW (1997) Glottal-to-noise excitation ratio-a new measure for describing pathological voices. Acta Acust United Acust 83(4):700–706

    Google Scholar 

  43. 43.

    Muhammad G, Alhamid MF, Hossain MS, Almogren AS, Vasilakos AV (2017) Enhanced living by assessing voice pathology using a co-occurrence matrix. Sensors 17(2):267

    Article  Google Scholar 

  44. 44.

    Muhammad G, Alsulaiman M, Ali Z, Mesallam TA, Farahat M, Malki KH, Al-nasheri A, Bencherif MA (2017) Voice pathology detection using interlaced derivative pattern on glottal source excitation. Biomed Signal Process Control 31:156–164

    Article  Google Scholar 

  45. 45.

    Murphy KP (2006) Naive bayes classifiers. University of British Columbia

  46. 46.

    Oates J (2009) Auditory-perceptual evaluation of disordered voice quality. Folia Phoniatr Logop 61(1):49–56

    Article  Google Scholar 

  47. 47.

    Parsa V, Jamieson DG (2003) Identification of pathological voices using glottal noise measures. J Speech Lang Hear Res 23(2):469–485

    Article  Google Scholar 

  48. 48.

    Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay E (2011) Scikit-learn: machine learning in python. J Mach Learn Res 12:2825–2830

    MathSciNet  MATH  Google Scholar 

  49. 49.

    Pimentel MA, Clifton DA, Clifton L, Tarassenko L (2014) A review of novelty detection. Sig Process 99:215–249

    Article  Google Scholar 

  50. 50.

    Reynolds D (2015) Gaussian mixture models. Encyclopedia of biometrics, pp 827–832

  51. 51.

    Sabir B, Rouda F, Khazri Y, Touri B, Moussetad M (2017) Improved algorithm for pathological and normal voices identification. Int J Electr Comput Eng (IJECE) 7(1):238–243

    Article  Google Scholar 

  52. 52.

    Saldanha JC, Ananthakrishna T, Pinto R (2014) Vocal fold pathology assessment using mel-frequency cepstral coefficients and linear predictive cepstral coefficients features. J Med Imaging Health Inf 4(2):168–173

    Article  Google Scholar 

  53. 53.

    Schalkoff RJ (1997) Artificial neural networks, vol 1. McGraw-Hill, New York

    Google Scholar 

  54. 54.

    Song P (2013) Assessment of vocal cord function and voice disorders. In: Principles and practice of interventional pulmonology, pp 137–149. Springer

  55. 55.

    Souissi N, Cherif A (2015) Dimensionality reduction for voice disorders identification system based on mel frequency cepstral coefficients and support vector machine. In: 2015 7th international conference on modelling, identification and control (ICMIC), pp 1–6. IEEE

  56. 56.

    Souissi N, Cherif A (2016) Speech recognition system based on short-term cepstral parameters, feature reduction method and artificial neural networks. In: 2016 2nd international conference on advanced technologies for signal and image processing (ATSIP), pp 667–671. IEEE

  57. 57.

    Stathopoulos ET, Huber JE, Sussman JE (2011) Changes in acoustic characteristics of the voice across the life span: measures from individuals 4–93 years of age. J Speech Lang Hear Res 54(4):1011–1021

    Article  Google Scholar 

  58. 58.

    Teager H (1980) Some observations on oral air flow during phonation. IEEE Trans Acoust Speech Signal Process 28(5):599–601

    Article  Google Scholar 

  59. 59.

    Titze IR (1994) Principles of voice production. Prentice-Hall, Englewood Cliffs

    Google Scholar 

  60. 60.

    Tsanas A, Little MA, McSharry PE, Ramig LO (2010) Nonlinear speech analysis algorithms mapped to a standard metric achieve clinically useful quantification of average Parkinson’s disease symptom severity. J R Soc Interface 8(59):842–855

    Article  Google Scholar 

  61. 61.

    Uloza V, Vegiene A, Saferis V (2015) Correlation between the quantitative video laryngostroboscopic measurements and parameters of multidimensional voice assessment. Biomed Signal Process Control 17(Suppl C):3–10

    Article  Google Scholar 

  62. 62.

    Woldert-Jokisz B (2007) Saarbruecken voice database

  63. 63.

    Wyse L (2017) Audio spectrogram representations for processing with convolutional neural networks. arXiv preprint arXiv:1706.09559

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This study was funded by the grant of the Czech Ministry of Health 16-30805A (Effects of non-invasive brain stimulation on hypokinetic dysarthria, micrographia, and brain plasticity in patients with Parkinson’s disease) and the following projects: SIX (CZ.1.05/2.1.00/03.0072) and LO1401. For the research, infrastructure of the SIX Center was used. The authors (P. Harar, Z. Galaz) of this study also acknowledge the financial support of Erwin Schrödinger International Institute for Mathematics and Physics during their stay at the “Systematic approaches to deep learning methods for audio” workshop held from 11 September, 2017, to 15 September, 2017, in Vienna.

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Correspondence to Pavol Harar.

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Harar, P., Galaz, Z., Alonso-Hernandez, J.B. et al. Towards robust voice pathology detection. Neural Comput & Applic 32, 15747–15757 (2020).

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  • Voice pathology detection
  • Deep learning
  • Gradient boosting
  • Anomaly detection