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      Predicting couple therapy outcomes based on speech acoustic features

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          Abstract

          Automated assessment and prediction of marital outcome in couples therapy is a challenging task but promises to be a potentially useful tool for clinical psychologists. Computational approaches for inferring therapy outcomes using observable behavioral information obtained from conversations between spouses offer objective means for understanding relationship dynamics. In this work, we explore whether the acoustics of the spoken interactions of clinically distressed spouses provide information towards assessment of therapy outcomes. The therapy outcome prediction task in this work includes detecting whether there was a relationship improvement or not (posed as a binary classification) as well as discerning varying levels of improvement or decline in the relationship status (posed as a multiclass recognition task). We use each interlocutor’s acoustic speech signal characteristics such as vocal intonation and intensity, both independently and in relation to one another, as cues for predicting the therapy outcome. We also compare prediction performance with one obtained via standardized behavioral codes characterizing the relationship dynamics provided by human experts as features for automated classification. Our experiments, using data from a longitudinal clinical study of couples in distressed relations, showed that predictions of relationship outcomes obtained directly from vocal acoustics are comparable or superior to those obtained using human-rated behavioral codes as prediction features. In addition, combining direct signal-derived features with manually coded behavioral features improved the prediction performance in most cases, indicating the complementarity of relevant information captured by humans and machine algorithms. Additionally, considering the vocal properties of the interlocutors in relation to one another, rather than in isolation, showed to be important for improving the automatic prediction. This finding supports the notion that behavioral outcome, like many other behavioral aspects, is closely related to the dynamics and mutual influence of the interlocutors during their interaction and their resulting behavioral patterns.

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          THE USE OF CONFIDENCE OR FIDUCIAL LIMITS ILLUSTRATED IN THE CASE OF THE BINOMIAL

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            A comparison of methods for multiclass support vector machines.

            Support vector machines (SVMs) were originally designed for binary classification. How to effectively extend it for multiclass classification is still an ongoing research issue. Several methods have been proposed where typically we construct a multiclass classifier by combining several binary classifiers. Some authors also proposed methods that consider all classes at once. As it is computationally more expensive to solve multiclass problems, comparisons of these methods using large-scale problems have not been seriously conducted. Especially for methods solving multiclass SVM in one step, a much larger optimization problem is required so up to now experiments are limited to small data sets. In this paper we give decomposition implementations for two such "all-together" methods. We then compare their performance with three methods based on binary classifications: "one-against-all," "one-against-one," and directed acyclic graph SVM (DAGSVM). Our experiments indicate that the "one-against-one" and DAG methods are more suitable for practical use than the other methods. Results also show that for large problems methods by considering all data at once in general need fewer support vectors.
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              Thin slices of expressive behavior as predictors of interpersonal consequences: A meta-analysis.

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                Author and article information

                Contributors
                Role: Formal analysisRole: SoftwareRole: Writing – original draft
                Role: ConceptualizationRole: Data curationRole: ResourcesRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole: MethodologyRole: Project administrationRole: SupervisionRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: SupervisionRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                2017
                21 September 2017
                : 12
                : 9
                : e0185123
                Affiliations
                [1 ] Department of Electrical Engineering, University of Southern California, Los Angeles, United States of America
                [2 ] Department of Psychology, University of Utah, Salt Lake City, Utah, United States of America
                University of Kent, UNITED KINGDOM
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                http://orcid.org/0000-0002-0790-7161
                Article
                PONE-D-16-39957
                10.1371/journal.pone.0185123
                5608311
                28934302
                63317e18-da3b-428c-ad73-ec3b3656e9ac
                © 2017 Nasir et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 6 October 2016
                : 6 September 2017
                Page count
                Figures: 3, Tables: 9, Pages: 23
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/100000001, National Science Foundation;
                Award ID: 1059095
                Award Recipient :
                Funding was provided by NSF award 1059095.
                Categories
                Research Article
                Biology and Life Sciences
                Behavior
                Physical Sciences
                Physics
                Acoustics
                Social Sciences
                Linguistics
                Speech
                Engineering and Technology
                Signal Processing
                Speech Signal Processing
                Medicine and Health Sciences
                Otorhinolaryngology
                Laryngology
                Speech-Language Pathology
                Speech Therapy
                Physical Sciences
                Physics
                Acoustics
                Bioacoustics
                Biology and Life Sciences
                Bioacoustics
                Physical Sciences
                Physics
                Acoustics
                Acoustic Signals
                Biology and Life Sciences
                Psychology
                Emotions
                Social Sciences
                Psychology
                Emotions
                Custom metadata
                This study represents a secondary data analysis of past data collections in couple therapy sessions, and thus cannot be fully anonymized. As a result, data cannot be released to the public. Additionally, raw data were made available to the authors from a third party (see below). The raw data contain sensitive participant information and include audio and video interactions. Interested researchers with proper IRB approvals may request the data in the same manner that the authors did. The PI for the study is: Andrew Christensen, Professor of Psychology, University of California, Los Angeles (UCLA). The authors can help facilitate IRB access to the data and anyone interested can contact: Brian Robert Baucom, Professor of Psychology, University of Utah, brian.baucom@ 123456utah.edu .

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