Proceedings of the Annual Meeting of the Cognitive Science Society

We explored use of Recurrence Quantification Analysis (RQA) of speech rhythm data from mental-health counseling sessions for prediction of quality of psychotherapy. Time-series of inter-syllable intervals (ISIs) were extracted from 239 counseling sessions conducted by 12 therapists who repeatedly interacted with 30 clients. We found a negative association between recurrence metrics and client-rated session quality and a negative link between percent of laminarity and therapist-rated session quality, after controlling for self-reported client depression and distress measures and duration of speech sound within a session. Placing value on reduced recurrence in patterns of ISIs, and especially reduced degree of a dyadic system remaining in the same speech-rhythm pattern may be indicative of a desire for variation in content and strategies of client-therapist interaction. These exploratory findings point to the possibility of RQA-based automated systems to capture the ‘footprint’ of the non-verbal dynamic that is indicative of successful mental-health counseling.