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Going Beyond the Cookie Theft Picture Test: Detecting Cognitive Impairments Using Acoustic Features

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Text, Speech, and Dialogue (TSD 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13502))

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Abstract

Standardized tests play a crucial role in the detection of cognitive impairment. Previous work demonstrated that automatic detection of cognitive impairment is possible using audio data from a standardized picture description task. The presented study goes beyond that, evaluating our methods on data taken from two standardized neuropsychological tests, namely the German SKT and a German version of the CERAD-NB, and a semi-structured clinical interview between a patient and a psychologist. For the tests, we focus on speech recordings of three sub-tests: reading numbers (SKT 3), interference (SKT 7), and verbal fluency (CERAD-NB 1). We show that acoustic features from standardized tests can be used to reliably discriminate cognitively impaired individuals from non-impaired ones. Furthermore, we provide evidence that even features extracted from random speech samples of the interview can be a discriminator of cognitive impairment. In our baseline experiments, we use OpenSMILE features and Support Vector Machine classifiers. In an improved setup, we show that using wav2vec 2.0 features instead, we can achieve an accuracy of up to 85%.

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Notes

  1. 1.

    Research approved by the Ethics Committee of the Nuremberg Hospital under File No. IRB-2021-021; each subject gave informed consent prior to recording.

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Authors and Affiliations

  1. Technische Hochschule Nürnberg Georg Simon Ohm, Nuremberg, Germany

    Franziska Braun, Korbinian Riedhammer & Sebastian P. Bayerl

  2. Psychiatrische Klinik und Psychotherapie, Universitätsklinikum Erlangen, Erlangen, Germany

    Andreas Erzigkeit

  3. Klinik für Psychiatrie und Psychotherapie, Universitätsklinik der Paracelsus Medizinischen Privatuniversität, Klinikum Nürnberg, Nuremberg, Germany

    Hartmut Lehfeld & Thomas Hillemacher

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  1. Franziska Braun
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  4. Thomas Hillemacher
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  5. Korbinian Riedhammer
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  6. Sebastian P. Bayerl
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Correspondence to Franziska Braun .

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Editors and Affiliations

  1. Faculty of Informatics, Masaryk University, Brno, Czech Republic

    Petr Sojka

  2. Faculty of Informatics, Masaryk University, Brno, Czech Republic

    Aleš Horák

  3. Faculty of Informatics, Masaryk University, Brno, Czech Republic

    Ivan Kopeček

  4. Faculty of Informatics, Masaryk University, Brno, Czech Republic

    Karel Pala

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Braun, F., Erzigkeit, A., Lehfeld, H., Hillemacher, T., Riedhammer, K., Bayerl, S.P. (2022). Going Beyond the Cookie Theft Picture Test: Detecting Cognitive Impairments Using Acoustic Features. In: Sojka, P., Horák, A., Kopeček, I., Pala, K. (eds) Text, Speech, and Dialogue. TSD 2022. Lecture Notes in Computer Science(), vol 13502. Springer, Cham. https://doi.org/10.1007/978-3-031-16270-1_36

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  • DOI: https://doi.org/10.1007/978-3-031-16270-1_36

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