Automated detection of the competency of delivering guided self-help for anxiety via speech and language processing

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.