An Easy Approach for the Classification of Children’s Voices based on the Fundamental Frequency Estimation

Laura Verde, Giuseppe De Pietro, Giovanna Sannino

2016

Abstract

Voice disorders, also called dysphonia, are qualitative and quantitative alterations of the voice. These pathologies, unfortunately, affect from 6% to 38% of children in the world. Voice disorders may have a negative impact on communication effectiveness, social development and self-esteem. The first weapon against the diffusion and the worsening of these pathologies is prevention. Acoustic analysis is one of the most important tools to appraise the state of health of a voice. It provides information about the possible presence of voice disorders by evaluating specific parameters like the Fundamental Frequency. In this paper we present an easy approach based on a mobile application for voice screening in children. The app provides a robust methodology for the fundamental frequency estimation of the voice signal by analysing in real time a child’s signal. It consists of a continuous vocalization of the vowel /a/ of five seconds in length. The methodology is also able to evaluate undesired noise that can alter the Fundamental Frequency estimation and the correct classification of the evaluated voice signal as pathological or healthy.

References

  1. Angelillo, I. F., Di Costanzo, B., Costa, G., Barillari, M., and Barillari, U. (2015). Epidemiological study on vocal disorders in paediatric age. Journal of preventive medicine and hygiene, 49(1).
  2. Baki, M. M., Wood, G., Alston, M., Ratcliffe, P., Sandhu, G., Rubin, J. S., and Birchall, M. A. (2013). Comparison between operavox and mdvp: Preliminary results. Otolaryngology-Head and Neck Surgery , 149(2 suppl):P203-P204.
  3. Belafsky, P. C., Postma, G. N., and Koufman, J. A. (2002). Validity and reliability of the reflux symptom index (rsi). Journal of Voice, 16(2):274-277.
  4. Boersma, P. (1993). Accurate short-term analysis of the fundamental frequency and the harmonics-to-noise ratio of a sampled sound. In Proceedings of the institute of phonetic sciences, volume 17, pages 97-110. Amsterdam.
  5. Cafazzo, J. A., Casselman, M., Katzman, D. K., and Palmert, M. R. (2012). 133. bant: An mhealth app for adolescent type i diabetes-a pilot study. Journal of Adolescent Health, 50(2):S77-S'.
  6. Camacho, A. and Harris, J. G. (2008). A sawtooth waveform inspired pitch estimator for speech and music. The Journal of the Acoustical Society of America, 124(3):1638-1652.
  7. Casper, J. K. and Leonard, R. (2006). Understanding voice problems: A physiological perspective for diagnosis and treatment. Lippincott Williams & Wilkins.
  8. Cooney, O. (1998). Acoustic analysis of the effects of alcohol on the human voice. PhD thesis, Dublin City University.
  9. De Cheveigné, A. and Kawahara, H. (2002). Yin, a fundamental frequency estimator for speech and music. The Journal of the Acoustical Society of America, 111(4):1917-1930.
  10. Deal, R. E., McClain, B., and Sudderth, J. F. (1976). Identification, evaluation, therapy, and follow-up for children with vocal nodules in a public school setting. Journal of speech and hearing disorders, 41(3):390- 397.
  11. Dejonckere, P. (1999). Voice problems in children: pathogenesis and diagnosis. International journal of pediatric otorhinolaryngology, 49:S311-S314.
  12. Forti, S., Amico, M., Zambarbieri, A., Ciabatta, A., Assi, C., Pignataro, L., and Cantarella, G. (2014). Validation of the italian voice handicap index-10. Journal of Voice, 28(2):263-e17.
  13. Glaze, L. E. (1996). Treatment of voice hyperfunction in the pre-adolescent. Language, Speech, and Hearing services in schools, 27(3):244-250.
  14. Gonzalez, J. and Carpi, A. (2004). Early effects of smoking on the voice: A multidimensional study. Medical Science Monitor, 10(12):CR649-CR656.
  15. Hunter, E. J., Tanner, K., and Smith, M. E. (2011). Gender differences affecting vocal health of women in vocally demanding careers. Logopedics Phoniatrics Vocology, 36(3):128-136.
  16. Johnstone, T. and Scherer, K. R. (1999). The effects of emotions on voice quality. In Proceedings of the XIVth International Congress of Phonetic Sciences, pages 2029-2032. University of California, Berkeley San Francisco.
  17. Kahane, J. C. and Mayo, R. (1989). The need for aggressive pursuit of healthy childhood voices. Language, Speech, and Hearing Services in Schools, 20(1):102- 107.
  18. King, D., Greaves, F., Exeter, C., and Darzi, A. (2013). gamification: Influencing health behaviours with games. Journal of the Royal Society of Medicine, 106(3):76-78.
  19. Kong, A. P.-H. (2015). Conducting cognitive exercises for early dementia with the use of apps on ipads. Communication Disorders Quarterly, 36(2):102-106.
  20. Leeper, L. H. (1992). Diagnostic examination of children with voice disordersa low-cost solution. Language, Speech, and Hearing Services in Schools, 23(4):353- 360.
  21. Lorant, V., Soto, V. E., Alves, J., Federico, B., Kinnunen, J., Kuipers, M., Moor, I., Perelman, J., Richter, M., Rimpelä, A., et al. (2015). Smoking in school-aged adolescents: design of a social network survey in six european countries. BMC research notes, 8(1):91.
  22. Lucchini, A. R. M. . E. (2002). La valutazione soggettiva ed oggettiva della disfonia: il protocollo sifel. In presented at the Relazione ufficiale al XXXVI Congresso Nazionale della Societ Italiana di Foniatria e Logopedia.
  23. Martínez, D., Lleida, E., Ortega, A., Miguel, A., and Villalba, J. (2012). Voice pathology detection on the saarbruecken voice database with calibration and fusion of scores using multifocal toolkit. In Advances in Speech and Language Technologies for Iberian Languages, pages 99-109. Springer.
  24. McCallum, S. (2012). Gamification and serious games for personalized health. Stud Health Technol Inform, 177:85-96.
  25. McNamara, A. P. and Perry, C. K. (1994). Vocal abuse prevention practicesa national survey of school-based speech-language pathologists. Language, Speech, and Hearing services in schools, 25(2):105-111.
  26. Miller, A. S., Cafazzo, J. A., and Seto, E. (2014). A game plan: Gamification design principles in mhealth applications for chronic disease management. Health informatics journal, page 1460458214537511.
  27. Naylor, P., Kounoudes, A., Gudnason, J., Brookes, M., et al. (2007). Estimation of glottal closure instants in voiced speech using the dypsa algorithm. Audio, Speech, and Language Processing, IEEE Transactions on, 15(1):34-43.
  28. Nerrière, E., Vercambre, M.-N., Gilbert, F., and KovessMasféty, V. (2009). Voice disorders and mental health in teachers: a cross-sectional nationwide study. BMC Public Health, 9(1):370.
  29. Nicollas, R., Garrel, R., Ouaknine, M., Giovanni, A., Nazarian, B., and Triglia, J.-M. (2008). Normal voice in children between 6 and 12 years of age: database and nonlinear analysis. Journal of voice, 22(6):671- 675.
  30. Pinilla, J., Gonzalez, B., Barber, P., and Santana, Y. (2002). Smoking in young adolescents: an approach with multilevel discrete choice models. Journal of epidemiology and community health, 56(3):227-232.
  31. Ross, M. J., Shaffer, H. L., Cohen, A., Freudberg, R., and Manley, H. J. (1974). Average magnitude difference function pitch extractor. Acoustics, Speech and Signal Processing, IEEE Transactions on, 22(5):353-362.
  32. Simons-Morton, B., Haynie, D. L., Crump, A. D., Eitel, P., and Saylor, K. E. (2001). Peer and parent influences on smoking and drinking among early adolescents. Health Education & Behavior, 28(1):95-107.
  33. Sun, X. (2002). Pitch determination and voice quality analysis using subharmonic-to-harmonic ratio. In Acoustics, Speech, and Signal Processing (ICASSP), 2002 IEEE International Conference on, volume 1, pages I-333. IEEE.
  34. Talkin, D. (1995). A robust algorithm for pitch tracking (rapt). Speech coding and synthesis, 495:518.
  35. Tan, L. and Karnjanadecha, M. (2003). Pitch detection algorithm: autocorrelation method and amdf.
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Paper Citation


in Harvard Style

Verde L., De Pietro G. and Sannino G. (2016). An Easy Approach for the Classification of Children’s Voices based on the Fundamental Frequency Estimation . In Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: SmartMedDev, (BIOSTEC 2016) ISBN 978-989-758-170-0, pages 570-577. DOI: 10.5220/0005849005700577


in Bibtex Style

@conference{smartmeddev16,
author={Laura Verde and Giuseppe De Pietro and Giovanna Sannino},
title={An Easy Approach for the Classification of Children’s Voices based on the Fundamental Frequency Estimation},
booktitle={Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: SmartMedDev, (BIOSTEC 2016)},
year={2016},
pages={570-577},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005849005700577},
isbn={978-989-758-170-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: SmartMedDev, (BIOSTEC 2016)
TI - An Easy Approach for the Classification of Children’s Voices based on the Fundamental Frequency Estimation
SN - 978-989-758-170-0
AU - Verde L.
AU - De Pietro G.
AU - Sannino G.
PY - 2016
SP - 570
EP - 577
DO - 10.5220/0005849005700577