8.8 Results

(iqr25, q75 − q25). Except for the feature amplitude, the features and statistical parameters were computed over a time window of 40 msec., using a step length of 10 msec.; that is, computing each feature every 10 msec. over the next 40 msec. of the signal. Hence, in total 65 (i.e., 5 × 13) parameters were determined from the five speech signal features.

8.8 Results

We analyzed the Stress-Provoking Story study and the Re-Living study separately. The analyses were the same for both studies; with both studies, the SUD scores were reviewed and an acoustic profile was generated.

The acoustic profiles were created with an LRM [262]. An LRM is an optimal linear model of the relationship between one dependent variable (e.g., the SUD) and several independent variables (e.g., the speech features). An LRM typically takes the following form:

y = β0 + β1x1 + · · · + βpxp + ε,

where ε represents unobserved random noise, and p represents the number of predictors (i.e., independent variables x and regression coefficients β). The linear regression equation is the result of a linear regression analysis, which aims to solve the following n equations in an optimal fashion. For more information on LRM, we refer to Appendix A.

It was expected that the acoustic profiles would benefit from a range of parameters derived from the five features, as it is known that various features and their parameters have independent contributions to the speech signal [369]. In order to create a powerful LRM, backward elimination/selection was applied to reduce the number of predictors. With backward elimination/selection, first all relevant features/parameters are added as predictors to the model (the so-called enter method), followed by multiple iterations removing each predictor for which p < α does not hold [155, 262]. In this research, we chose α = .1, as the (arbitrary) threshold for determining whether or not a variable had a significant contribution to predicting subjective stress.

The backward elimination/selection stops when for all remaining predictors in the model, p < α is true. As the backward method uses the relative contribution to the model as selection criteria, the interdependency of the features is taken into account as well. This makes it a robust method for selecting the most relevant features and their parameters. This is crucial for creating a strong model, because it has been shown that inclusion of too many features can reduce the power of a model [142]. As the general practice of reporting the explained variance of a regression model, R2, does not take this into account, the adjusted R2, R2 was computed as well. The R2 penalizes the addition of extra predictors to the model, and, therefore, is always equal to or lower than R2.

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