Attas, D., Power, N., Smithies, J. et al. (5 more authors) (2022) Automated detection of the competency of delivering guided self-help for anxiety via speech and language processing. Applied Sciences, 12 (17). 8608. ISSN 2076-3417
Abstract
Speech and language play an essential role in automatically assessing several psychotherapeutic qualities. These automation procedures require translating the manual rating qualities to speech and language features that accurately capture the assessed psychotherapeutic quality. Speech features can be determined by analysing recordings of psychotherapeutic conversations (acoustics), while language-based analyses rely on the transcriptions of such psychotherapeutic conversations (linguistics). Guided self-help is a psychotherapeutic intervention that mainly relay on therapeutic competency of practitioners. This paper investigates the feasibility of automatically analysing guided self-help sessions for mild-to-moderate anxiety to detect and predict practitioner competence. This analysis is performed on sessions drawn from a patient preference randomised controlled trial using actual patient-practitioner conversations manually rated using a valid and reliable measure of competency. The results show the efficacy and potential of automatically detecting practitioners’ competence using a system based on acoustic and linguistic features extracted from transcripts generated by an automatic speech recogniser. Feature extraction, feature selection and classification or regression have been implemented as blocks of the prediction model. The Lasso regression model achieved the best prediction results with an R of 0.92 and lower error rates with an MAE of 1.66 and RMSE of 2.25.
Metadata
Item Type: | Article |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
Keywords: | competency; guided self-help sessions; automatic speech recognition; machine learning; speech processing; language processing |
Dates: |
|
Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) The University of Sheffield > Faculty of Medicine, Dentistry and Health (Sheffield) > School of Health and Related Research (Sheffield) > ScHARR - Sheffield Centre for Health and Related Research |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 22 Sep 2022 09:54 |
Last Modified: | 22 Sep 2022 09:54 |
Status: | Published |
Publisher: | MDPI AG |
Refereed: | Yes |
Identification Number: | 10.3390/app12178608 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:190651 |