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Predicting a Cold from Speech Using Fisher Vectors; SVM and XGBoost as Classifiers

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Speech and Computer (SPECOM 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12335))

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Abstract

Screening a cold may be beneficial in the sense of avoiding the propagation of it. In this study, we present a technique for classifying subjects having a cold by using their speech. In order to achieve this goal, we make use of frame-level representations of the recordings of the subjects. Such representations are exploited by a generative Gaussian Mixture Model (GMM) which consequently produces a fixed-length encoding, i.e. Fisher vectors, based on the Fisher Vector (FV) approach. Afterward, we compare the classification performance of the two algorithms: a linear kernel SVM and a XGBoost Classifier. Due to the data sets having a high class imbalance, we undersample the majority class. Applying Power Normalization (PN) and Principal Component Analysis (PCA) on the FV features proved effective at improving the classification score: SVM achieved a final score of 67.81% of Unweighted Average Recall (UAR) on the test set. However, XGBoost gave better results on the test set by just using raw Fisher vectors; and with this combination we achieved a UAR score of 70.43%. The latter classification approach outperformed the original (non-fused) baseline score given in ‘The INTERSPEECH 2017 Computational Paralinguistics Challenge’.

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Acknowledgments

This study was partially funded by the National Research, Development and Innovation Office of Hungary via contract NKFIH FK-124413 and by the Ministry for Innovation and Technology, Hungary (grant TUDFO/47138-1/2019-ITM). G. Gosztolya was also funded by the János Bolyai Scholarship of the Hungarian Academy of Sciences and by the Hungarian Ministry of Innovation and Technology New National Excellence Program ÚNKP-20-5.

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Authors and Affiliations

  1. University of Szeged, Institute of Informatics, Szeged, Hungary

    José Vicente Egas-López & Gábor Gosztolya

  2. MTA-SZTE Research Group on Artificial Intelligence, Szeged, Hungary

    Gábor Gosztolya

Authors
  1. José Vicente Egas-López
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  2. Gábor Gosztolya
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Corresponding author

Correspondence to José Vicente Egas-López .

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Editors and Affiliations

  1. St. Petersburg Institute for Informatics and Automation, Russian Academy of Sciences, St. Petersburg, Russia

    Alexey Karpov

  2. Institute for Applied and Mathematical Linguistics, Moscow State Linguistic University, Moscow, Russia

    Rodmonga Potapova

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Egas-López, J.V., Gosztolya, G. (2020). Predicting a Cold from Speech Using Fisher Vectors; SVM and XGBoost as Classifiers. In: Karpov, A., Potapova, R. (eds) Speech and Computer. SPECOM 2020. Lecture Notes in Computer Science(), vol 12335. Springer, Cham. https://doi.org/10.1007/978-3-030-60276-5_15

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  • DOI: https://doi.org/10.1007/978-3-030-60276-5_15

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  • Online ISBN: 978-3-030-60276-5

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