Skip to main content
  • Research Article
  • Open access
  • Published:

Study of Harmonics-to-Noise Ratio and Critical-Band Energy Spectrum of Speech as Acoustic Indicators of Laryngeal and Voice Pathology

Abstract

Acoustic analysis of speech signals is a noninvasive technique that has been proved to be an effective tool for the objective support of vocal and voice disease screening. In the present study acoustic analysis of sustained vowels is considered. A simple-means nearest neighbor classifier is designed to test the efficacy of a harmonics-to-noise ratio (HNR) measure and the critical-band energy spectrum of the voiced speech signal as tools for the detection of laryngeal pathologies. It groups the given voice signal sample into pathologic and normal. The voiced speech signal is decomposed into harmonic and noise components using an iterative signal extrapolation algorithm. The HNRs at four different frequency bands are estimated and used as features. Voiced speech is also filtered with 21 critical-bandpass filters that mimic the human auditory neurons. Normalized energies of these filter outputs are used as another set of features. The results obtained have shown that the HNR and the critical-band energy spectrum can be used to correlate laryngeal pathology and voice alteration, using previously classified voice samples. This method could be an additional acoustic indicator that supplements the clinical diagnostic features for voice evaluation.

References

  1. Titze IR: Principles of Voice Production. Prentice-Hall, Englewood Cliffs, NJ, USA; 1994.

    Google Scholar 

  2. Hirano M, Hibi S, Terasawa R, Fujiu M: Relationship between aerodynamic, vibratory, acoustic and psychoacoustic correlates in dysphonia. Journal of Phonetics 1986, 14: 445–456.

    Google Scholar 

  3. Davis SB: Acoustic characteristics of laryngeal pathology. In Speech Evaluation in Medicine. Edited by: Darby J. Grune and Stratton, New York, NY, USA; 1981:77–104.

    Google Scholar 

  4. Hansen JHL, Gavidia-Ceballos L, Kaiser JF: A nonlinear operator-based speech feature analysis method with application to vocal fold pathology assessment. IEEE Transactions on Biomedical Engineering 1998,45(3):300–313. 10.1109/10.661155

    Article  Google Scholar 

  5. Fujimura O, Hirano M: Vocal Fold Physiology-Voice Quality Control. Singular, San Diego, Calif, USA; 1995.

    Google Scholar 

  6. Baken RJ, Orlikoff RF: Clinical Measurements of Speech and Voice. Singular Thomson Learning, San Diego, Calif, USA; 2000.

    Google Scholar 

  7. Kent RD, Read C: The Acoustic Analysis of Speech. AITBS, New Delhi, India; 1995.

    Google Scholar 

  8. Gavidia-Ceballos L, Hansen JHL: Direct speech feature estimation using an iterative EM algorithm for vocal fold pathology detection. IEEE Transactions on Biomedical Engineering 1996,43(4):373–383. 10.1109/10.486257

    Article  Google Scholar 

  9. Childers DG: Signal processing methods for the assessment of vocal disorders. The Journal of Biomedical Engineering Society of India 1994, 13: 117–130.

    Google Scholar 

  10. Pinto NB, Titze IR: Unification of perturbation measures in speech signals. The Journal of the Acoustical Society of America 1990,87(3):1278–1289. 10.1121/1.398803

    Article  Google Scholar 

  11. Yumoto E, Gould WJ, Baer T: Harmonics to noise ratio as an index of the degree of hoarseness. The Journal of the Acoustical Society of America 1982,71(6):1544–1550. 10.1121/1.387808

    Article  Google Scholar 

  12. Kasuya H, Ogawa S, Mashima K, Ebihara S: Normalized noise energy as an acoustic measure to evaluate pathologic voice. The Journal of the Acoustical Society of America 1986,80(5):1329–1334. 10.1121/1.394384

    Article  Google Scholar 

  13. Manfredi C: Adaptive noise energy estimation in pathological speech signals. IEEE Transactions on Biomedical Engineering 2000,47(11):1538–1543. 10.1109/10.880107

    Article  Google Scholar 

  14. de Oliveira Rosa M, Pereira JC, Grellet M: Adaptive estimation of residue signal for voice pathology diagnosis. IEEE Transactions on Biomedical Engineering 2000,47(1):96–104. 10.1109/10.817624

    Article  Google Scholar 

  15. Plant F, Kessler H, Cheetham B, Earis J: Speech monitoring of infective laryngitis. Proceedings of the 4th International Conference on Spoken Language Processing (ICSLP '96), October 1996, Philadelphia, Pa, USA 2: 749–752.

    Article  Google Scholar 

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

    Google Scholar 

  17. Michaelis D, Fröhlich M, Strube HW: Selection and combination of acoustic features for the description of pathologic voices. The Journal of the Acoustical Society of America 1998,103(3):1628–1639. 10.1121/1.421305

    Article  Google Scholar 

  18. krishna Anantha, Shama K, Niranjan UC:-Means nearest neighbor classifier for voice pathology. Proceedings of IEEE India Annual Conference (INDICON '04), December 2004, IIT-Kharagpur, India 232–234.

    Google Scholar 

  19. Zwicker E, Fastl H: Psycho-Acoustics: Facts and Models. Springer, Berlin, Germany; 1999.

    Google Scholar 

  20. Kay Elemetrics Corp, Disordered Voice Database Model 4337, Version 1.03, Massachusetts Eye and Ear Infirmary Voice and Speech Lab, 2002

  21. Yegnanarayana B, d'Alessandro C, Darsinos V: An iterative algorithm for decomposition of speech signals into periodic and aperiodic components. IEEE Transactions on Speech and Audio Processing 1998,6(1):1–11. 10.1109/89.650304

    Article  Google Scholar 

  22. Wendt C, Petropulu A: Pitch determination and speech segmentation using the discrete wavelet transform. Proceedings of IEEE International Symposium on Circuits and Systems (ISCAS '96), May 1996, Atlanta, Ga, USA 2: 45–48.

    Google Scholar 

  23. Mallat S, Zhong S: Characterization of signals from multiscale edges. IEEE Transactions on Pattern Analysis and Machine Intelligence 1992,14(7):710–732. 10.1109/34.142909

    Article  Google Scholar 

  24. Quatieri TF: Discrete-Time Speech Signal Processing. Prentice Hall PTR, Upper Saddle River, NJ, USA; 2002.

    Google Scholar 

  25. Chen SH, Wang JF: Noise-robust pitch detection method using wavelet transform with aliasing compensation. IEE Proceedings 2002,149(6):327–334.

    Google Scholar 

  26. Papoulis A: Signal Analysis. Int. edition. McGraw-Hill, New York, NY, USA; 1984.

    MATH  Google Scholar 

  27. Parikh GK, Loizou PC: The effects of noise on the spectrum of speech. a M.S. thesis presented to the faculty of Telecommunication Engineering, University of Texas at Dallas, August 2002

  28. Yost WA: Fundamentals of Hearing. 3rd edition. Academic Press, New York, NY, USA; 1994.

    Google Scholar 

  29. Duda RO, Hart PE, Stork DG: Pattern Analysis. John Wiley & Sons, New York, NY, USA; 2002.

    Google Scholar 

  30. Boyanov B, Hadjitodorov S: Acoustic analysis of pathological voices. A voice analysis system for the screening of laryngeal diseases. IEEE Engineering in Medicine and Biology Magazine 1997,16(4):74–82. 10.1109/51.603651

    Article  Google Scholar 

  31. Alonso JB, de Leon J, Alonso I, Ferrer MA: Automatic detection of pathologies in the voice by HOS based parameters. EURASIP Journal on Applied Signal Processing 2001,2001(4):275–284. 10.1155/S1110865701000336

    Article  Google Scholar 

  32. Godino-Llorente JI, Gomez-Vilda P: Automatic detection of voice impairments by means of short-term cepstral parameters and neural network based detectors. IEEE Transactions on Biomedical Engineering 2004,51(2):380–384. 10.1109/TBME.2003.820386

    Article  Google Scholar 

  33. Umapathi K, Krishnan S, Parsa V, Jamieson DG: Discrimination of pathological voices using a time-frequency approach. IEEE Transactions on Biomedical Engineering 2005,52(3):421–430. 10.1109/TBME.2004.842962

    Article  Google Scholar 

  34. Boone DR: The Voice and Voice Therapy. Prentice-Hall, Englewood Cliffs, NJ, USA; 1988.

    Google Scholar 

  35. Koufman JA, Blalock PD: Functional voice disorders. Oto Laryngological Clinics of North America. Voice Disorders, October 1991, Philadelphia, Pa, USA 24(5):1059–1073.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Kumara Shama.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License ( https://creativecommons.org/licenses/by/2.0 ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Reprints and permissions

About this article

Cite this article

Shama, K., krishna, A. & 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. Adv. Signal Process. 2007, 085286 (2006). https://doi.org/10.1155/2007/85286

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1155/2007/85286

Keywords