Einsatz von Sprachverarbeitungstechnologien in der Logopädie und Sprachtherapie

Ansätze der Lernforschung zeigen, dass nutzer*innengerichtete Rückmeldungen innerhalb digitaler Übungsanwendungen notwendig sind, um die Therapiewirksamkeit zu steigern. In der logopädischen Versorgung wird dies in der Face-to-Face-Therapie von dem*der Therapeut*in übernommen. In digitalen Anwendungen kann die Analyse und Bewertung der Sprachproduktion durch Sprachverarbeitungstechnologien, z. B. automatischer Spracherkennung, umgesetzt werden.

Abstract

Approaches from learning research show that user-directed feedback within digital applications is necessary to increase therapy effectiveness. In face-to-face therapy context this is done by the therapist. In digital applications, assessment of speech production can be implemented through speech processing technologies. Automatic speech recognition is e. g. used in apps to assess speech intelligibility. Other acoustic methods enable the analysis of prosodic features. Particularly challenging is the variance of pathological speech production and the digital implementation of practice applications. This calls for interdisciplinary projects in order to use digital speech processing solutions for different target groups.

Schlüsselwörter

digitale Sprachverarbeitung - Sprachverarbeitungstechnologien - individuelles Feedback - maschinelles Lernen - digitale Logopädie

Keywords

digital speech processing - speech processing technologies - individual feedback - machine learning - digital speech and language therapy

Publication History

Article published online:
08 March 2022

• Literatur

• 1 Starke A, Mülhaus J. Evidenzanspruch in der Anwendung von Applikationen in der Sprachtherapie. In: Bilda K, Mülhaus J, Ritterfeld U. Neue Technologien in der Sprachtherapie. Stuttgart: Thieme; 2017: 110-116
• 2 Ritterfeld U. Psychologische Grundlagen. Bilda K, Mülhaus J, Ritterfeld U. Neue Technologien in der Sprachtherapie. Stuttgart: Thieme; 2017: 44-55 DOI: 10.1055/b-004-129736
• 3 Hirsh-Pasek K, Zosh JM, Golinkoff RM. et al. Putting Education in “Educational” Apps: Lessons From the Science of Learning. Psychol Sci Public Interest 2015; 16: 3-34 DOI: 10.1177/1529100615569721.
• 4 Grund CK, Schelkle M. Visualisieren spielend erlernen – Ein Serious Game zur Verbesserung von Managementberichten. In: Strahringer S, Leyh C. Gamification und Serious Games. Wiesbaden: Springer; 2017: 167-181 DOI: 10.1007/978-3-658-16742-4_13
• 5 Nakamura J, Csikszentmihalyi M. The Concept of Flow. In: Flow and the Foundations of Positive Psychology. Dordrecht: Springer; 2014: 239-263 DOI: 10.1007/978-94-017-9088-8_16
• 6 Postma A. Detection of errors during speech production: a review of speech monitoring models. Cognition 2000; 77: 97-132 DOI: 10.1016/s0010-0277(00)00090-1.
• 7 Mühlhaus J, Frieg H, Bilda K, Ritterfeld U. Game-Based Speech Rehabilitation for People with Parkinson’s Disease. In: Antona M, Stephanidis C. (eds) Universal Access in Human-Computer Interaction. Human and Technological Environments. UAHCI 2017. Lecture Notes in Computer Science, vol 10279. Cham: Springer; 2017: 76-85 DOI: 10.1007/978-3-319-58700-4_7
• 8 Wahl M. Technische Grundlagen. In: Bilda K, Mülhaus J, Ritterfeld U. Neue Technologien in der Sprachtherapie. Stuttgart: Thieme; 2017: 56-64 DOI: 10.1055/b-004-129736
• 9 Latif S, Qadir J, Qayyum A. et al. Speech Technology for Healthcare: Opportunities, Challenges, and State of the Art. IEEE Rev Biomed Eng 2021; 14: 342-356 DOI: 10.1109/RBME.2020.3006860.
• 10 Keshet J. Automatic speech recognition: A primer for speech-language pathology researchers. Int J Speech Lang Pathol 2018; 20: 599-609 DOI: 10.1080/17549507.2018.1510033.
• 11 Frieg H, Mühlhaus J, Ritterfeld U. et al. ISi-Speech: A Digital Training System for Acquired Dysarthria. Stud Health Technol Inform 2017; 242: 330-334 DOI: 10.3233/978-1-61499-798-6-330.
• 12 Shahin M, Zafar U, Ahmed B. The Automatic Detection of Speech Disorders in Children: Challenges, Opportunities, and Preliminary Results. IEEE J Select Topics Signal Proc 2020; 14: 400-412 DOI: 10.1109/jstsp.2019.2959393.
• 13 Jacks A, Haley KL, Bishop G. et al. Automated Speech Recognition in Adult Stroke Survivors: Comparing Human and Computer Transcriptions. Folia Phoniatr Logop 2019; 71: 286-296 DOI: 10.1159/000499156.