Abstract
The goal of this research is to answer the question: is it necessary to build a completely different system in order to automatically recognize functional dysphonia (FD) in children’s cases or is it possible to train the system with healthy and pathological voices of adults? For this reason preliminary statistical analyses were carried out between healthy and functional dysphonia voices of children and healthy children voices with healthy adults’. The statistical analyses draw the conclusion that variations of Jitter and Shimmer values with Harmonics-to-Noise Ratio (HNR) and the first component of the mel-frequency cepstral coefficients (MFCC1) are good indicators to separate Healthy and FD voices in case of children as well. Healthy samples of children and adult voices were compared giving the clear conclusion that differences exist in the examined acoustical parameters even between healthy child and healthy adult groups. It is necessary to carry out the investigations separately on children’s voices as well, we cannot use adult voices to make any conclusions to children’s voices. Lastly the differences between adult female and male samples were examined. The study results confirmed our assumptions that in order to build an automatic decision making system that recognizes FD it is advisable to build separate systems for adult males, adult females and children.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Hirschberg, J., Hacki, T., és Mészáros, K.: Foniátria és társtudományok: A hangképzés, a beszéd és a nyelv, a hallás és a nyelés élettana, kórtana, diagnosztikája és terápiája (I. kötet). Budapest: Eötvös Kiadó (2013)
Schulze, J.: Stimmstörungen im Kindes-und Jugendalter. Schulz-Kirchner, Idstein (2002)
Connor, N.P., et al.: Attitudes of children with dysphonia. J. Voice 22(2), 197–209 (2008)
Zhang, Yu., Jiang, J.J.: Acoustic analyses of sustained and running voices from patients with laryngeal pathologies. J. Voice 22(1), 1–9 (2008)
Peng, C., et al.: Pathological voice classification based on a single Vowel’s acoustic features. In: 7th IEEE International Conference on Computer and Information Technology. IEEE (2007)
Parsa, V., Jamieson, D.G.: Acoustic discrimination of pathological Voice Sustained vowels versus continuous speech. J. Speech, Lang., Hear. Res. 44(2), 327–339 (2001)
Askenfelt, A.G., Hammarberg, B.: Speech waveform perturbation analysis a perceptual-acoustical comparison of seven measures. J. Speech, Lang., Hear. Res. 29(1), 50–64 (1986)
Peng, C., et al.: Pathological voice classification based on a single Vowel’s acoustic features. In: 7th IEEE International Conference on Computer and Information Technology. IEEE (2007)
Ritchings, R.T., McGillion, M., Moore, C.J.: Pathological voice quality assessment using artificial neural networks. Med. Eng. Phys. 24(7), 561–564 (2002)
Klára, V., Viktor, I., Krisztina, M.: Voice disorder detection on the basis of continuous speech. In: Jobbágy, Á. (ed.) 5th European Conference of the International Federation for Medical and Biological Engineering. IFMBE Proceedings, vol. 37, pp. 86–89. Springer, Heidelberg (2011)
Klára, V.: SAMPA computer readable phonetic alphabet, Hungarian (2008)
Boersma, P., Weenink, D.: Praat: doing phonetics by computer [Computer program]. Version 6.0.15 (2016). http://www.praat.org/
Kazinczi, F., Mészáros, K., Vicsi, K.: Automatic detection of voice disorders. In: Dediu, A.-H., et al. (eds.) SLSP 2015. LNCS, vol. 9449, pp. 143–152. Springer, Heidelberg (2015). doi:10.1007/978-3-319-25789-1_14
Fadem, B.: High-Yield Behavioral Science (High-Yield Series). Lippincott Williams & Wilkins, Hagerstwon (2008)
Imre, V.: Acoustical examination of pathological voices, Diploma work. Budapest University of Technology and Economics (2009)
Acknowledgement
We would like to thank Krisztina Mészáros from the Department of Head and Neck Surgery of the National Institute of Oncology for her continued cooperation in helping us collect and evaluate the patient data, which is the basis of our research. We would also like to thank Mária Ágostházy from the Speech Therapy and Vocational Education Service of Újbuda for helping us construct the Juvenile dysphonia and Healthy Child speech database. We hope that our cooperation will last long.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Tulics, M.G., Kazinczi, F., Vicsi, K. (2016). Statistical Analysis of Acoustical Parameters in the Voice of Children with Juvenile Dysphonia. In: Ronzhin, A., Potapova, R., Németh, G. (eds) Speech and Computer. SPECOM 2016. Lecture Notes in Computer Science(), vol 9811. Springer, Cham. https://doi.org/10.1007/978-3-319-43958-7_81
Download citation
DOI: https://doi.org/10.1007/978-3-319-43958-7_81
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-43957-0
Online ISBN: 978-3-319-43958-7
eBook Packages: Computer ScienceComputer Science (R0)