A psychometric investigation of "macroscopic" speech measures for clinical and psychological science.

The analysis of vocal expression is a critical endeavor for psychological and clinical sciences and is an increasingly popular application for computer-human interfaces. Despite this, and despite advances in the efficiency, affordability, and sophistication of vocal analytic technologies, there is considerable variability across studies regarding what aspects of vocal expression are studied. Vocal signals can be quantified in a myriad of ways, and their underlying structure, at least with respect to "macroscopic" measures from extended speech, is presently unclear. To address this issue, we evaluated the psychometric properties-notably, the structural and construct validity-of a systematically defined set of global vocal features. Our analytic strategy focused on (a) identifying redundant variables among this set, (b) employing principal components analysis (PCA) to identify nonoverlapping domains of vocal expression, (c) examining the degrees to which the vocal variables are modulated as a function of changes in speech task, and (d) evaluating the relationship between the vocal variables and cognitive (i.e., verbal fluency) and clinical (i.e., depression, anxiety, and hostility) variables. Spontaneous speech samples from 11 independent studies of young adults (>60 s in length), employing one of three different speaking tasks, were examined (N = 1,350). Confounding variables (i.e., sex, ethnicity) were statistically controlled for. The PCA identified six distinct domains of vocal expression. Collectively, vocal expression (defined in terms of these domains) was modulated as a function of speech task and was related to the cognitive and clinical variables. These findings provide empirically grounded implications for the study of vocal expression in psychological and clinical sciences.