It's not what you said, it's how you said it: An analysis of therapist vocal features during psychotherapy.

Emotional Expression Linguistics Psychotherapy Research Quantitative Analysis Vocal Acoustics

Journal

Counselling and psychotherapy research
ISSN: 1473-3145
Titre abrégé: Couns Psychother Res
Pays: England
ID NLM: 101599516

Informations de publication

Date de publication:
Mar 2023
Historique:
pmc-release: 01 03 2024
entrez: 6 3 2023
pubmed: 7 3 2023
medline: 7 3 2023
Statut: ppublish

Résumé

Psychotherapy is a conversation, whereby, at its foundation, many interventions are derived from the therapist talking. Research suggests that the voice can convey a variety of emotional and social information, and individuals may change their voice based on the context and content of the conversation (e.g., talking to a baby or delivering difficult news to patients with cancer). As such, therapists may adjust aspects of their voice throughout a therapy session depending on if they are beginning a therapy session and checking in with a client, conducting more therapeutic "work," or ending the session. In this study, we modeled three vocal features-pitch, energy, and rate-with linear and quadratic multilevel models to understand how therapists' vocal features change throughout a therapy session. We hypothesized that all three vocal features would be best fit with a quadratic function - starting high and more congruent with a conversational voice, decreasing during the middle portions of therapy where more therapeutic interventions were being administered, and increasing again at the end of the session. Results indicated a quadratic model for all three vocal features was superior in fitting the data, as compared to a linear model, suggesting that therapists begin and end therapy using a different style of voice than in the middle of a session.

Identifiants

pubmed: 36873916
doi: 10.1002/capr.12489
pmc: PMC9979575
mid: NIHMS1857798
doi:

Types de publication

Journal Article

Langues

eng

Pagination

258-269

Subventions

Organisme : NIAAA NIH HHS
ID : R01 AA018673
Pays : United States

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Auteurs

Christina S Soma (CS)

Department of Educational Psychology, University of Utah, Salt Lake City, (UT) USA.

Dillon Knox (D)

Viterbi Department of Computer Science, University of Southern California, Los Angeles, (CA,) USA.
Ming Hsieh Department of Electrical Engineering, University of Southern California, Los Angeles, (CA,) USA.

Timothy Greer (T)

Viterbi Department of Computer Science, University of Southern California, Los Angeles, (CA,) USA.

Keith Gunnerson (K)

Department of Educational Psychology, University of Utah, Salt Lake City, (UT) USA.

Alexander Young (A)

Viterbi Department of Computer Science, University of Southern California, Los Angeles, (CA,) USA.

Shrikanth Narayanan (S)

Viterbi Department of Computer Science, University of Southern California, Los Angeles, (CA,) USA.

Classifications MeSH