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Identification of Voice Disorders: A Comparative Study of Machine Learning Algorithms

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Speech and Computer (SPECOM 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14338))

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Abstract

A voice disorder is a state that influences the quality, loudness, or pitch of a person’s voice. Classifying voice disorders automatically by non-invasive methods can help doctors to diagnose voice disorders quickly and more effectively. Machine Learning (ML) algorithms play a role of non-invasive methods to automatically classify the voice disorders using voice samples. This study compares different ML algorithms trained with spectral features for the classification of voice samples as healthy or pathological. The experiments are conducted using the sustained samples of the vowel /a/ of healthy and disordered voice, selected from Saarbruecken Voice Database (SVD). As the selected subset is imbalanced, various resampling methods are explored to balance the dataset. The performance of the classifiers are evaluated in terms of accuracy, precision, recall, and F1-score. Among the proposed models, Random Forest (RF) and Extreme Gradient Boosting (XGBoost) algorithms resampled with SMOTE-ENN have shown very promising accuracies of 0.902 and 0.906, respectively.

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Notes

  1. 1.

    https://www.geeksforgeeks.org/ml-bagging-classifier/.

  2. 2.

    https://www.javatpoint.com/k-nearest-neighbor-algorithm-for-machine-learning.

  3. 3.

    https://librosa.org/doc/main/index.html.

  4. 4.

    https://imbalanced-learn.org/stable/.

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Authors and Affiliations

  1. Mangalore University, Mangalore, India

    Sharal Coelho & Hosahalli Lakshmaiah Shashirekha

Authors
  1. Sharal Coelho
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  2. Hosahalli Lakshmaiah Shashirekha
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Corresponding author

Correspondence to Sharal Coelho .

Editor information

Editors and Affiliations

  1. St. Petersburg Federal Research Center of the Russian Academy of Sciences, St. Petersburg, Russia

    Alexey Karpov

  2. Koneru Lakshmaiah Education Foundation, Vaddeswaram, India

    K. Samudravijaya

  3. Indian Institute of Information Technology Dharwad, Dharwad, India

    K. T. Deepak

  4. Indian Institute of Technology Dharwad, Dharwad, India

    Rajesh M. Hegde

  5. KIIT Group of Colleges, Gurugram, India

    Shyam S. Agrawal

  6. Indian Institute of Technology Dharwad, Dharwad, India

    S. R. Mahadeva Prasanna

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Coelho, S., Shashirekha, H.L. (2023). Identification of Voice Disorders: A Comparative Study of Machine Learning Algorithms. In: Karpov, A., Samudravijaya, K., Deepak, K.T., Hegde, R.M., Agrawal, S.S., Prasanna, S.R.M. (eds) Speech and Computer. SPECOM 2023. Lecture Notes in Computer Science(), vol 14338. Springer, Cham. https://doi.org/10.1007/978-3-031-48309-7_45

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  • DOI: https://doi.org/10.1007/978-3-031-48309-7_45

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-48308-0

  • Online ISBN: 978-3-031-48309-7

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