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Towards Automated Assessment of Stuttering and Stuttering Therapy

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Text, Speech, and Dialogue (TSD 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12284))

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Abstract

Stuttering is a complex speech disorder that can be identified by repetitions, prolongations of sounds, syllables or words and blocks while speaking. Severity assessment is usually done by a speech therapist. While attempts at automated assessment were made, it is rarely used in therapy. Common methods for the assessment of stuttering severity include percent stuttered syllables (%SS), the average of the three longest stuttering symptoms during a speech task or the recently introduced Speech Efficiency Score (SES). This paper introduces the Speech Control Index (SCI), a new method to evaluate the severity of stuttering. Unlike SES, it can also be used to assess therapy success for fluency shaping. We evaluate both SES and SCI on a new comprehensively labeled dataset containing stuttered German speech of clients prior to, during and after undergoing stuttering therapy. Phone alignments of an automatic speech recognition system are statistically evaluated in relation to their relative position to labeled stuttering events. The results indicate that phone length distributions differ in respect to their position in and around labeled stuttering events.

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Notes

  1. 1.

    Available at https://www.uclass.psychol.ucl.ac.uk/uclassfsf.htm.

  2. 2.

    Kaldi recipe available at https://github.com/uhh-lt/kaldi-tuda-de.

  3. 3.

    G2P tool available online at https://www-i6.informatik.rwth-aachen.de/web/Software/g2p.html.

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Acknowledgements

The authors thank the Institut der Kasseler Stottertherapie for their support and excellent collaboration. This work is supported by a research grant of the Bayerisches Staatsministerium für Bildung und Kultus, Wissenschaft und Kunst as well as the BAYWiss (Bayerisches Wissenschaftsforum).

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Authors and Affiliations

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

    Sebastian P. Bayerl & Korbinian Riedhammer

  2. Institut der Kasseler Stottertherapie, Bad Emstal, Germany

    Florian Hönig & Joëlle Reister

Authors
  1. Sebastian P. Bayerl
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  2. Florian Hönig
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  3. Joëlle Reister
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  4. Korbinian Riedhammer
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Corresponding author

Correspondence to Sebastian P. Bayerl .

Editor information

Editors and Affiliations

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

    Petr Sojka

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

    Ivan Kopeček

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

    Karel Pala

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

    Aleš Horák

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Bayerl, S.P., Hönig, F., Reister, J., Riedhammer, K. (2020). Towards Automated Assessment of Stuttering and Stuttering Therapy. In: Sojka, P., Kopeček, I., Pala, K., Horák, A. (eds) Text, Speech, and Dialogue. TSD 2020. Lecture Notes in Computer Science(), vol 12284. Springer, Cham. https://doi.org/10.1007/978-3-030-58323-1_42

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  • DOI: https://doi.org/10.1007/978-3-030-58323-1_42

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-58322-4

  • Online ISBN: 978-3-030-58323-1

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