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Trends in Voice Recording Classification - Comparison of Conventional Features and Image Analysis Approach

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Data Science and Algorithms in Systems (CoMeSySo 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 597))

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Abstract

Voice pathology includes a wide selection of diseases that affect human voice. Moreover, surgeries in the vicinity of the voice apparatus might temporarily negatively influence human voice. Despite the fact that voice pathology represents a common health issue, the majority of diagnostic methods are either invasive or subject to an interpretation of the medical professional which might vary between individuals. This article presents features that have been traditionally used in aiding the voice pathology diagnosis as well as modern approach to the voice pathology detection using image analysis and classification.

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Acknowledgements

Jan Vrba acknowledges his specific university research grant JIGA 445-85-2222. Jakub Steinbach acknowledges his specific university grant (IGA) 445-88-2202.

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Authors and Affiliations

  1. Department of Computing and Control Engineering, University of Chemistry and Technology in Prague, Technická 1905/5, 166 28, Praha 6, Czech Republic

    Jakub Steinbach & Jan Vrba

  2. Faculty of Arts, Institute of Phonetics, Charles University, náměstí Jana Palacha 2, 116 38, Praha 1, Czech Republic

    Richard Mazúr

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  1. Jakub Steinbach
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Correspondence to Jakub Steinbach .

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Editors and Affiliations

  1. Faculty of Applied Informatics, Tomas Bata University in Zlín, Zlín, Czech Republic

    Radek Silhavy

  2. Faculty of Applied Informatics, Tomas Bata University in Zlín, Zlín, Czech Republic

    Petr Silhavy

  3. Faculty of Applied Informatics, Tomas Bata University in Zlín, Zlín, Czech Republic

    Zdenka Prokopova

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Steinbach, J., Mazúr, R., Vrba, J. (2023). Trends in Voice Recording Classification - Comparison of Conventional Features and Image Analysis Approach. In: Silhavy, R., Silhavy, P., Prokopova, Z. (eds) Data Science and Algorithms in Systems. CoMeSySo 2022. Lecture Notes in Networks and Systems, vol 597. Springer, Cham. https://doi.org/10.1007/978-3-031-21438-7_51

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