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Acoustic Analysis for Vocal Fold Assessment—Challenges, Trends, and Opportunities

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Data Science in Applications

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1084))

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Abstract

The goal of this study was a review of trends in non-invasive vocal fold assessment to identify the significance of acoustic analysis within the scope of proposed methods. A review protocol for selected relevant studies was developed using systematic review guidelines. A classification scheme was applied to process the selected relevant study set, data were extracted and mapped in a systematic map. A systematic map was used to synthesize data for a quantitative summary of the main research question. A tabulated summary was created to summarize supporting topics. Results show that non-invasive vocal fold assessment is influenced by general computer science trends. Machine learning techniques dominate studies and publications, i.e., 51% of the set used at least one method to detect and classify vocal fold pathologies.

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Authors and Affiliations

  1. Institute of Data Science and Digital Technologies, Vilnius University, Vilnius, Lithuania

    Monika Danilovaitė & Gintautas Tamulevičius

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  1. Monika Danilovaitė
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  2. Gintautas Tamulevičius
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Correspondence to Monika Danilovaitė .

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  1. Institute of Data Science and Digital Technologies, Vilnius University, Vilnius, Lithuania

    Gintautas Dzemyda

  2. Institute of Data Science and Digital Technologies, Vilnius University, Vilnius, Lithuania

    Jolita Bernatavičienė

  3. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Janusz Kacprzyk

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Danilovaitė, M., Tamulevičius, G. (2023). Acoustic Analysis for Vocal Fold Assessment—Challenges, Trends, and Opportunities. In: Dzemyda, G., Bernatavičienė, J., Kacprzyk, J. (eds) Data Science in Applications. Studies in Computational Intelligence, vol 1084. Springer, Cham. https://doi.org/10.1007/978-3-031-24453-7_8

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