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Feedback Vocal Rehabilitation Software Applied to Mobile Devices for Maximum Phonation Time

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Advanced Machine Learning Technologies and Applications (AMLTA 2021)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1339))

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Abstract

The key to the maximum phonation time (MPT) is how much lung capacity you have. If the MPT is too short, one cannot cut the sentences inappropriate time and lead to communication difficulties. However, there is no research on the effect of using feedback software for vocal rehabilitation in the past, especially for those who cannot increase their vital capacity through exercise. Therefore, we designed two tests for the experiment in our study. First, using mobile devices to collect MPT and Intensity Level (IL) data in a quiet space of 40 adults whose cognitive status is normal, and analyzing their characteristic changes. Then, we use the mobile device to perform MPT feedback rehabilitation and collect two characteristic data again. The purpose of our study is to understand the effects of using feedback vocal rehabilitation software.

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References

  1. Chen, S.H.: Sex differences in frequency and intensity in reading and voice range profiles for Taiwanese adult speakers. Folia Phoniatr Logop 59(1), 1–9 (2007)

    Article  Google Scholar 

  2. Johnson, A., Goldfine, A.: Intrasubject reliability of maximum phonation time. J. Voice 30(6), 775.e1-775.e4 (2016)

    Article  Google Scholar 

  3. Ferrand, C.T.: Speech Science: An Integrated Approach to Theory and Clini-cal Practice. 2nd edn. Pearson/Allyn and Bacon, University of Michigan, America (2007)

    Google Scholar 

  4. Nina, K.: Sex differences in sound processing. Hear. J. 72(10), 6 (2019)

    Google Scholar 

  5. Awan, S.N., Novaleski, C.K., Yingling, J.R.: Test-retest reliability for aerodynamic measures of voice. J. Voice 27(6), 674–684 (2013)

    Article  Google Scholar 

  6. Rosenthal, A.L., Lowell, S.Y., Colton, R.H.: Aerodynamic and acoustic features of vocal effort. J. Voice 28(2), 144–153 (2014)

    Article  Google Scholar 

  7. Nishio, M., Tanaka, Y., Niimi, S.: Analysis of age-related changes in the acoustic characteristics of the voice. Jpn. J. Logopedics Phoniatrics 50(1), 6–13 (2009)

    Article  Google Scholar 

  8. Yamauchi, A., Yokonishi, H., Imagawa, H., Sakakibara, K., Nito, T., Tayama, N., Yamasoba, T.: Age-and gender-related difference of vocal fold vibration and glot-tal configuration in normal speakers: analysis with glottal area waveform. J. Voice 28(5), 525–531 (2014)

    Article  Google Scholar 

  9. Jeng, J.Y.: Norm data related to Chinese speech ability. https://www.jengspeech.com/wp/. Accessed 25 Aug 2020

  10. Teodora, D.G., Aneta, K., Magdalena, I.C.: Speech emotion recognition based on voice fundamental frequency. Arch. Acoust. 44(2), 277–286 (2019)

    Google Scholar 

  11. Armstrong, M., Vogatzis, L.: Personalized exercise training in chronic lung diseases. Respiratory 24(9), 854–862 (2019)

    Google Scholar 

  12. Näsström, A.-K., Schalling, E.: Development of a method for assessment of dysarthria in a foreign language: a pilot study. Logopedics Phoniatrics Vocology 45(1), 39–48 (2020)

    Google Scholar 

  13. de Souza, L.B.R., Pereira, R.M., dos Santos, M.M., de Almeida Godoy, C.M.: Fundamental frequency, phonation maximum time and vocal complaints in morbidly obese women. Arq Bras Cir Dig 27(1), 43–46 (2014)

    Google Scholar 

  14. Lewandowski, A., Gillespie, A.I., Kridgen, S., Jeong, K., Yu, L., Gartner Schmidt, J.: Adult normative data for phonatory aerodynamics in connected speech. Laryngoscope 128(4), 909–914 (2018)

    Article  Google Scholar 

  15. Gu, W., Fan, P., Liu, W.: Acoustic analysis of Mandarin speech in Parkinson’s disease with the effects of Levodopa. In: Fang, Q., Dang, J., Perrier, P., Wei, J., Wang, L., Yan, N. (eds.), Studies on Speech Production, ISSP 2017, pp. 211–224. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00126-1_19

  16. Hegland, K.W., Troche, M., Brandimore, A.: Relationship between respiratory sensory perception, speech and swallow in Parkinson’s disease. Mov. Disord. 6(3), 243–249 (2019)

    Article  Google Scholar 

  17. Jeng, J.Y., Weismer, G., Kent, R.D.: Production and perception of mandarin tone in adults with cerebral palsy. Clin. Linguist. Phonetics 20(1), 67–87 (2006)

    Article  Google Scholar 

  18. Kalathottukaren, R.T., Purdy, S.C., Ballard, E.: Prosody perception and musical pitch discrimination in adults using cochlear implants. Int. J. Audiol. 54(7), 444–452 (2015)

    Article  Google Scholar 

Download references

Acknowledgment

This work was supported in part by the “Allied Advanced Intelligent Biomedical Research Center, STUST” from Higher Education Sprout Project, Ministry of Education, Taiwan.

Author information

Authors and Affiliations

  1. Department of Computer Science and Information Engineering, Institute of Manufacturing Information and Systems, National Cheng Kung University, Tainan, Taiwan

    Jia-Yi Lin & Chao-Chun Chen

  2. Graduate Institute of Audiology and Speech Therapy, National Kaohsiung Normal University, Kaohsiung, Taiwan

    Yu-Chi Huang

  3. Department of Computer Science and Information Engineering, Southern Taiwan University of Science and Technology, Tainan, Taiwan

    Gwo-Jiun Horng

  4. Chi Mei Hospital, Tainan, Taiwan

    Chien-Cin Hsu

Authors
  1. Jia-Yi Lin
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  2. Yu-Chi Huang
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  3. Gwo-Jiun Horng
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  4. Chien-Cin Hsu
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  5. Chao-Chun Chen
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Corresponding author

Correspondence to Gwo-Jiun Horng .

Editor information

Editors and Affiliations

  1. Information Technology Department, Cairo University, Computer and Information Faculty, Giza, Egypt

    Aboul-Ella Hassanien

  2. College of Information Science and Engineering, Fujian University of Technology, Fuzhou, Fujian, China

    Kuo-Chi Chang

  3. International Center for Informatics Research, Beijing Jaiotong University, Beijing, China

    Tang Mincong

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Lin, JY., Huang, YC., Horng, GJ., Hsu, CC., Chen, CC. (2021). Feedback Vocal Rehabilitation Software Applied to Mobile Devices for Maximum Phonation Time. In: Hassanien, AE., Chang, KC., Mincong, T. (eds) Advanced Machine Learning Technologies and Applications. AMLTA 2021. Advances in Intelligent Systems and Computing, vol 1339. Springer, Cham. https://doi.org/10.1007/978-3-030-69717-4_101

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