The Language of Interpersonal Interaction: An Interdisciplinary Approach to Assessing and Processing Vocal and Speech Data

The European Journal of Counselling Psychology, January 1, 2018 | Go to article overview

The Language of Interpersonal Interaction: An Interdisciplinary Approach to Assessing and Processing Vocal and Speech Data


Interpersonal interactions are challenging to study but are central to our daily lives and health and well-being. Consider the example of psychotherapy. A psychotherapy session is a complex interaction between a therapist and a patient, and when successful, something in the dyadic interaction leads to a reduction in psychiatric symptoms and an increase in the patient’s functioning and well-being. Yet, the raw ‘data’ of psychotherapy is the verbal (i.e. acoustic, semantic) and non-verbal behavior of the two people, and these data streams are continuous throughout the psychotherapy session. The inherent complexity of such data has typically led researchers to use methods that drastically simplify the field’s understanding of this complex process (Baucom, 2010).

Verbal and non-verbal data streams are not specific to psychotherapy but are common to most interpersonal interactions, which are a focus of study across a wide array of scholarly disciplines. Moreover, researchers use the tools they know and are often unaware of novel methodologies developed in other disciplines. The lack of interdisciplinary collaboration on methods for studying interpersonal interaction has slowed the pace of scientific discovery. In addition, it likely contributes to the propagation of systematic errors associated with the limitations of current methods. Within psychology, the most common method for studying interpersonal interaction is observational behavior coding. In this method, behavior is quantified by human coders (sometimes called annotators or raters) according to rules developed by investigators to capture the essential aspects of the dyadic interaction. Using this methodology, trained coders watch or listen to a dyad’s interacting and score them on dimensions defined by the coding system. However, behavioral coding has a number of shortcomings (Baucom & Iturralde, 2012): (a) it is time consuming, often involving months of training prior to the actual work; (b) it can be error prone as human coders do not always agree with each other on coding decisions; (c) behavioral coding does not ‘scale up’ to larger samples due to time constraints; (d) behavioral coding systems often do not translate cross-culturally (Zimmermann, Baucom, Irvine, & Heinrichs, 2015), impeding replication and generalization of findings; and (e) the coding systems are simplifications of the true complexity of interactions because raters are limited in the quantity and temporal specificity of factors they can observe and code during a dyad’s interactions. What is needed is a new set of methods for studying interpersonal interaction that allow us to move beyond these limitations.

Fortunately, methods and tools to work with complex linguistic data exist within engineering and computer science, falling broadly within the categories of speech signal processing and statistical text-mining and natural language processing (Busso, Lee, & Narayanan, 2009; Gaut, Steyvers, Imel, Atkins, & Smyth, 2017). At a fundamental level, signal processing techniques use computer algorithms to derive “informative” quantities from highly multivariate, continuous streams of input. One of the tremendous advantages of signal processing methods is that they can be used to process vast amounts of information far beyond what an individual rater could garner from watching a dyadic interaction. In much the same way that observational coders are trained to recognize and rate classes of behaviors, speech signal processing and statistical text-mining use algorithms and models to estimate mathematical quantities, called features, that characterize aspects of the original signal, either voice or text. Some features provide a psychologically meaningful measurement of behavior in and of themselves (e.g. vocally encoded emotional arousal measured by the fundamental frequency [f0] of the speech sound wave) while other features can be combined to recognize social behaviors of interest with statistical techniques. …

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