Journal of Voice
Volume 24, Issue 1 , Pages 47-56 , January 2010

The Effectiveness of the Glottal to Noise Excitation Ratio for the Screening of Voice Disorders

  • Juan Ignacio Godino-Llorente

      Affiliations

    • Department of Circuits & Systems Engineering, Universidad Politécnica de Madrid, Madrid, Spain
    • Corresponding Author InformationCorresponding author. Department of Circuits & Systems Engineering, Universidad Politécnica de Madrid Ctra., Valencia, km. 7, 28031 Madrid, Spain.
  • ,
  • Víctor Osma-Ruiz

      Affiliations

    • Department of Circuits & Systems Engineering, Universidad Politécnica de Madrid, Madrid, Spain
  • ,
  • Nicolás Sáenz-Lechón

      Affiliations

    • Department of Circuits & Systems Engineering, Universidad Politécnica de Madrid, Madrid, Spain
  • ,
  • Pedro Gómez-Vilda

      Affiliations

    • Department of Circuits & Systems Engineering, Universidad Politécnica de Madrid, Madrid, Spain
  • ,
  • Manuel Blanco-Velasco

      Affiliations

    • Universidad de Alcalá, Department of Signal Theory and Communication, Alcatá de Henares, Madrid, Spain
  • ,
  • Fernando Cruz-Roldán

      Affiliations

    • Universidad de Alcalá, Department of Signal Theory and Communication, Alcatá de Henares, Madrid, Spain

Received 7 March 2008 ,Accepted 22 April 2008.

References 

  1. Boyanov B, Hadjitodorov S. Acoustic analysis of pathological voices. A voice analysis system for the screening of laryngeal diseases. IEEE Eng Med Biol Mag. 1997;16:74–82
  2. Kasuya H, Ogawa S, Mashima K, Ebihara S. Normalized noise energy as an acoustic measure to evaluate pathologic voice. J Acoust Soc Am. 1986;80:1329–1334
  3. Manfredi C. Adaptive noise energy estimation in pathological speech signals. IEEE Trans Biomed Eng. 2000;47:1538–1543
  4. Qi Y, Hillman RE. Temporal and spectral estimations of harmonics-to-noise ratio in human voice signals. J Acoust Soc Am. 1997;102:537–543
  5. Yumoto E, Gould WJ, Baer T. Harmonics-to-noise ratio as an index of the degree of hoarseness. J Acoust Soc Am. 1982;71:1544–1550
  6. Michaelis D, Gramss T, Strube HW. Glottal-to-Noise Excitation ratio—a new measure for describing pathological voices. Acustica/Acta acustica. 1997;83:700–706
  7. de Krom GA. Cepstrum-based technique for determining a harmonics-to-noise ratio in speech signals. J Speech Hear Res. 1993;36:254–266
  8. Feijoo S, Hernández-Espinosa C. Short-term stability measures for the evaluation of vocal quality. J Speech Hear Res. 1990;33:324–334
  9. Baken RJ, Orlikoff R. Clinical Measurement of Speech and Voice, 2 edn. San Diego, CA: Publishing Group; 2000;
  10. Klingholtz F, Martin F. The measurement of the signal-to-noise ratio (SNR) in continuous speech. Speech Commun. 1987;6:15–26
  11. Qi Y, Weinberg B, Bi N, Hess W. Minimizing the effect of period determination on the computation of amplitude perturbation of voice. J Acoust Soc Am. 1995;97:2525–2532
  12. Deliyski D. In: Acoustic model and evaluation of pathological voice production in Proceedings of Eurospeech '93. vol. 3:Berlin: Germany; 1993;p. 1969–1972
  13. Prosek RA, Montgomery AA, Walden BE, Hawkins DB. An evaluation of residue features as correlates of voice disorders. J Commun Disord. 1987;20:105–117
  14. Martin D, Fitch J, Wolfe V. Pathologic voice type and the acoustic prediction of severity. J Speech Hear Res. 1995;38:765–771
  15. Parsa V, Jamieson DG. Acoustic discrimination of pathological voice: sustained vowels versus continuous speech. J Speech Lang Hear Res. 2001;44:327–339
  16. Yanagihara N. Significance of harmonic changes and noise components in hoarseness. J Speech Hear Res. 1967;10:531–541
  17. Parsa V, Jamieson DG. Identification of pathological voices using glottal noise measures. J Speech Lang Hear Res. 2000;43:469–485
  18. 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
  19. Yumoto E, Sasaki Y, Okamura H. Harmonics-to-noise ratio and psychophysical measurement of the degree of hoarseness. J Speech Hear Res. 1984;27:2–6
  20. Hadjitodorov S, Mitev P. A computer system for acoustic analysis of pathological voices and laryngeal disease screening. Med Eng Phys. 2002;24:419–429
  21. Ritchings RT, McGillion MA, Moore CJ. Pathological voice quality assessment using artificial neural networks. Med Eng Phys. 2002;24:561–564
  22. Michaelis D, Fröhlich M, Strube HW. Selection and combination of acoustic features for the description of pathologic voices. J Acoust Soc Am. 1998;103:1628–1639
  23. Fernández Liesa R, Damborenea Tajada J, Rueda Gormedino P, et al. Acoustic analysis of the normal voice in nonsmoking adults. Acta Otorrinolaringol Esp. 1999;50:134–141
  24. Damborenea Tajada J, Fernández Liesa R, Llorente Arenas E, et al. The effect of tobacco consumption on acoustic voice analysis. Acta Otorrinolaringol Esp. 1999;50:448–452
  25. Preciado JA, Fernández S. Digital analysis of the acoustic signal in vocal pathology diagnosis. Sensitivity and specificity of shimmer and jitter measurements. Acta Otorrinolaringol Esp. 1998;49:475–481
  26. González Álvarez T, Cervera T, Miralles JL. Acoustic analysis of speech: Reliability of a set of acoustic multidimensional parameters. Multidimensionales Acta Otorrinolaringol Esp. 2002;53:256–268
  27. Oppenheim AV, Schafer RW, Buck JR. Discrete-Time Signal Processing, 2 edn. Saddle River, NJ: Prentice Hall; 1999;
  28. Rabiner LR, Juang BH. Fundamentals of Speech Recognition. Englewood Cliffs, NJ: Prentice Hall; 1993;
  29. Kay Elemetrics Corp . Disordered Voice Database. Version 1.0. 3CD-ROM Lincoln Park, NJ: Kay Elemetrics Corp; 1994;
  30. Godino-Llorente JI, Gómez-Vilda P, Blanco-Velasco M. Dimensionality reduction of a pathological voice quality assessment system based on Gaussian mixture models and short-term cepstral parameters. IEEE Trans Biomed Eng. 2006;53:1943–1953
  31. Godino-Llorente JI, Sáenz Lechón N, Osma Ruíz V, Gómez-Vilda P, Aguilera S. An integrated tool for the evaluation of voice disorders. Med Eng Phys. 2006;28:276–289
  32. Martin AF, Doddington GR, Kamm T, Ordowski M, Przybocki MA. In: The DET curve in assessment of detection task performance in Proceedings of Eurospeech '97. vol. IV:Rhodes: Crete; 1997;p. 1895–1898
  33. Hanley JA, McNeil BJ. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology. 1982;143:29–36
  34. Hanley JA, McNeil BJ. A method of comparing the areas under receiver operating characteristics curves derived from the same cases. Radiology. 1983;148:839–843

PII: S0892-1997(08)00068-4

doi: 10.1016/j.jvoice.2008.04.006

Journal of Voice
Volume 24, Issue 1 , Pages 47-56 , January 2010