An analysis of observation length requirements in spoken language for machine understanding of human behaviors
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Automatic quantification of human interaction behaviors based on language information has been shown to be effective in psychotherapy research domains such as marital therapy and cancer care. Existing systems typically use a moving-window approach where the target behavior construct is first quantified based on observations inside a window, such as a fixed number of words or turns, and then integrated over all the windows in that interaction. Given a behavior of interest, it is important to employ the appropriate length of observation, since too short a window might not contain sufficient information. Unfortunately, the link between behavior and observation length for lexical cues has not been well studied and it is not clear how these requirements relate to the characteristics of the target behavior construct. Therefore, in this paper, we investigate how the choice of window length affects the efficacy of language-based behavior quantification, by analyzing (a) the similarity between system predictions and human expert assessments for the same behavior construct and (b) the consistency in relations between predictions of related behavior constructs. We apply our analysis to a large and diverse set of behavior codes that are used to annotate real-life interactions and find that behaviors related to negative affect can be quantified from just a few words whereas those related to positive traits and problem solving require much longer observation windows. On the other hand, constructs that describe dysphoric affect do not appear to be quantifiable from language information alone, regardless of how long they are observed. We compare our findings with related work on behavior quantification based on acoustic vocal cues as well as with prior work on thin slices and human personality predictions and find that, in general, they are in agreement.
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