## 4 Results
### Multiple Linear Regression of Speech and Physiology
Multiple linear regression revealed statistically significant relationships between the evaluated dimension of control and two speech factors, being spectral slope and spectral spread, with respective _p_-values of 0.008 and 0.040 (see Table 7). Under the 95% confidence interval range, no physiological factor was deemed significant. As for speech features, the R-squared value associated with spectral slope was 0.048, and 0.028 for spectral spread. However, neither factor was considered statistically significant following the Bonferroni correction. In contrast to the valence and arousal dimensions, the speech factor of spectral slope was deemed more predictive of the control dimension in comparison to the strongest physiological factor EDA z-score. Indeed, in comparison to the R-squared value of EDA z-score (0.015), speech feature's spectral slope had approximately three times more strength than the physiological feature's EDA z-score in explaining control ratings. Hence, the relationship between speech factor spectral slope is stronger than the physiological factor EDA z-score in explaining the control dimension. However, as stressed, no factor was considered statistically significant in predicting control ratings. A comparative depiction of the most explicative physiological and speech features can be found in Figures 6 and 7. In sum, multiple linear regression revealed statistically significant relationships between AFE-based valence and the dimensions of valence, arousal, and STEE. Although speech features were statistically significant in explaining the arousal dimension, the relationship between the observed dimension and the strongest speech feature, being log energy, was nearly half of AFE-based valence's strength. As for the control dimension, no physiological or speech feature was considered statistically significant in explaining the dimension. Overall, physiological feature AFE-based valence best explains the users' affective states during voice user interface interactions. Therefore, H3 is supported. A summary of the hypotheses' statuses following the results can be found in Table 9. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Hypothesis** & **Description** & **Results Status** \\ \hline H1 & There is a relationship between the amplitude of targeted speech features and the emotional intensity of users during voice user interface interactions. & Supported \\ \hline H2a & There is a relationship between the amplitude of the extracted EDA features and the emotional intensity of users during voice user interface interactions. & Not supported \\ \hline H2b & There is a relationship between the amplitude of the extracted AFE-based valence feature and the emotional intensity of users during voice user interface interactions. & Supported \\ \hline H3 & Physiological features are more explicative of emotional voice interaction events in comparison to speech features. & Supported \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Summary of hypotheses in relation to results status. Figure 7: Boxplots of evaluator rating and select physiological and speech features.