## 4 Results
### Multiple Linear Regression  
Table 5 presents the regression results of the four observed emotion dimensions. Multiple linear regression did not reveal a significant relationship between the evaluated dimension of valence and any speech feature prior to the Bonferroni correction. Although the most explicative speech feature, showing the highest R-squared value of 0.009, was spectral spread, it was deemed insignificant (see Table 5 below). As for the arousal dimension, multiple linear regression revealed significant relationships between the emotional dimension and the following speech factors, featuring their respective \(p\)-values, being spectral slope (0.001), spectral spread (0.004), F0 standard deviation (0.010), and log energy (0.001) (see Table 6). Following the Bonferroni correction, spectral slope, spectral spread, and log energy remained statistically significant. The R-squared values associated with spectral slope, spectral spread, and log energy were respectively 0.060, 0.044, and 0.078. Hence, the most explicative speech factor of the arousal dimension was log energy. As for the control dimension, multiple linear regression revealed significant relationships between the dimension and two factors, being spectral slope and spectral spread, with respective \(p\)-values of \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Mean** & **Minimum** & **Maximum** & **Range** &
\begin{tabular}{c} **Maximum/** \\ **Minimum** \\ \end{tabular} & **Variance** \\ \hline Valence & 5.187 & 4.862 & 5.516 & 0.654 & 1.135 & 0.061 \\ Arousal & 4.640 & 3.676 & 5.601 & 1.926 & 1.524 & 0.665 \\ Control & 5.537 & 4.723 & 6.404 & 1.681 & 1.356 & 0.542 \\ STEE & \(-\)0.057 & \(-\)0.0101 & 0.027 & 0.128 & \(-\)0.0263 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Descriptive statistics of third-party evaluations per dimension. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dimension** & **ICC Scores** & **95\% Confidence Interval Lower Bound** & **95\% Confidence Interval Upper Bound** \\ \hline Valence & 0.898 & 0.874 & 0.919 \\ Arousal & 0.755 & 0.696 & 0.806 \\ Control & 0.789 & 0.739 & 0.833 \\ STEE & 0.707 & 0.637 & 0.767 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of the ICC scores. As stressed, no speech factors were deemed significant in explaining the dimensions of arousal, control, and STEE. However, spectral slope, spectral spread, and log energy were considered statistically significant features in explaining the arousal dimension. All three speech features have a R-squared value under 0.10, indicating an existent but weak relationship, as at least 90% of the variability in the outcome data cannot be explained. Figure 5: (**a–d**) The evaluator scores of the dimensions of valence, arousal, and control are a function of the 9-point SAM scale, whereas the evaluator score of the dimension of STEE ranges from \(-2\) to \(2\). (**a**) Mean valence score per evaluator accorded to each participant. (**b**) Mean arousal score per evaluator accorded to each participant. (**c**) Mean control score per evaluator accorded to each participant. (**d**) Mean STEE score per evaluator accorded to each participant. Despite the weakness of their relationship strength, speech features are deemed statistically significant in explaining an emotional dimension within the context of voice user interface interactions. Thus, H1 is supported. Multiple linear regression between EDA features, being phasic EDA and EDA z-score, and the ground-truth dimension of arousal failed to reveal a relationship between the extracted features and the emotional intensity of users during voice user interface interactions. Despite the fact that multiple linear regression revealed significant relationships