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European Archives of Oto-Rhino-Laryngology

, Volume 272, Issue 11, pp 3391–3399 | Cite as

Exploring the feasibility of smart phone microphone for measurement of acoustic voice parameters and voice pathology screening

  • Virgilijus UlozaEmail author
  • Evaldas Padervinskis
  • Aurelija Vegiene
  • Ruta Pribuisiene
  • Viktoras Saferis
  • Evaldas Vaiciukynas
  • Adas Gelzinis
  • Antanas Verikas
Laryngology

Abstract

The objective of this study is to evaluate the reliability of acoustic voice parameters obtained using smart phone (SP) microphones and investigate the utility of use of SP voice recordings for voice screening. Voice samples of sustained vowel/a/obtained from 118 subjects (34 normal and 84 pathological voices) were recorded simultaneously through two microphones: oral AKG Perception 220 microphone and SP Samsung Galaxy Note3 microphone. Acoustic voice signal data were measured for fundamental frequency, jitter and shimmer, normalized noise energy (NNE), signal to noise ratio and harmonic to noise ratio using Dr. Speech software. Discriminant analysis-based Correct Classification Rate (CCR) and Random Forest Classifier (RFC) based Equal Error Rate (EER) were used to evaluate the feasibility of acoustic voice parameters classifying normal and pathological voice classes. Lithuanian version of Glottal Function Index (LT_GFI) questionnaire was utilized for self-assessment of the severity of voice disorder. The correlations of acoustic voice parameters obtained with two types of microphones were statistically significant and strong (r = 0.73–1.0) for the entire measurements. When classifying into normal/pathological voice classes, the Oral-NNE revealed the CCR of 73.7 % and the pair of SP-NNE and SP-shimmer parameters revealed CCR of 79.5 %. However, fusion of the results obtained from SP voice recordings and GFI data provided the CCR of 84.60 % and RFC revealed the EER of 7.9 %, respectively. In conclusion, measurements of acoustic voice parameters using SP microphone were shown to be reliable in clinical settings demonstrating high CCR and low EER when distinguishing normal and pathological voice classes, and validated the suitability of the SP microphone signal for the task of automatic voice analysis and screening.

Keywords

Acoustic analysis Voice screening Smart phone 

Notes

Acknowledgments

This study was supported by grant VP1-3.1- ŠMM-10-V-02-030 from the Ministry of Education and Science of Republic of Lithuania.

Compilance with ethical standards

Conflict of interest

No conflicts of interest to declare.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Virgilijus Uloza
    • 1
    Email author
  • Evaldas Padervinskis
    • 1
  • Aurelija Vegiene
    • 1
  • Ruta Pribuisiene
    • 1
  • Viktoras Saferis
    • 2
  • Evaldas Vaiciukynas
    • 3
  • Adas Gelzinis
    • 3
  • Antanas Verikas
    • 3
    • 4
  1. 1.Department of OtolaryngologyLithuanian University of Health SciencesKaunasLithuania
  2. 2.Department of Physics, Mathematics and BiophysicsLithuanian University of Health SciencesKaunasLithuania
  3. 3.Department of Electric Power SystemsKaunas University of TechnologyKaunasLithuania
  4. 4.Department of Intelligent SystemsHalmstad UniversityHalmstadSweden

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