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.

Highlights

  • Behavioral Signal Processing (BSP) [1, 2] refers to computational methods that support measurement, analysis, and modeling of human behavior and interactions

  • In addition to evaluating how well directly signal-derived acoustic features compare with manually derived behavioral codes as features for prediction, we evaluate the prediction of the outcome when both feature streams are used together

  • Experiments with different feature sets. For each of these aforementioned experiments, we investigate the performance of various feature sets extracted from pre- and post-therapy sessions: 1. acoustic features with static functionals, 2. acoustic features with dynamic functionals, 3. acoustic features, 4. manually(human)-derived behavioral codes as features, 5. all features

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Summary

Introduction

Behavioral Signal Processing (BSP) [1, 2] refers to computational methods that support measurement, analysis, and modeling of human behavior and interactions. Researchers have explored information gathered from various modalities such as vocal patterns of speech [3, 4, 10, 11], spoken language use [1, 12] and visual body gestures [13] These studies are promising towards the creation of automated support systems for psychotherapists in creating objective measures for diagnostics, intervention assessment and planning. This entails characterizing and understanding a range of clinically meaningful behavior traits and patterns but, critically, measure behavior change in response to treatment. In clinical psychology, predicting (or measuring from couple interactions, without couple, or therapist provided metrics) the outcome of the relationship of a couple undergoing counseling has been a subject of long-standing interest [14,15,16]

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