## 4 Results
### Multiple Linear Regression of Speech and Physiology  
As stressed, multiple linear regression revealed a relationship between the dimension of valence and speech feature spectral spread, with a R-squared value of 0.009. However, even prior to the Bonferroni correction, the relationship was deemed statistically insignificant. On the other hand, the multiple linear regression revealed a significant relationship between the evaluated dimension of valence and AFE-based valence (\(p<0.0001\)), with a R-squared value of 0.402 (see Table 5). Thus, when comparing R-squared values for spectral spread and AFE-based valence, the physiological measure had approximately 41 times more predictive power than voice feature when assessing valence ratings. The relationship between the valence dimension and the AFE-based valence was therefore stronger than any observed speech feature. As for arousal, multiple linear regression revealed a relationship between AFE-based valence and the dimension in question under 95% confidence interval range (\(p<0.0001\)) (see Table 6). Following Bonferroni correction, AFE-based valence remained statistically significant, with a R-squared value of 0.152. This was the sole extracted physiological feature that was considered statistically significant, as EDA z-score and phasic EDA did not achieve significance. Despite having fewer statistically significant factors, physiological measure AFE-based valence indicated a stronger relationship in comparison to significant speech features of spectral slope, spectral spread, and log energy. When comparing the R-squared value of AFE-based valence to the highest value amongst the statistically relevant speech features, being log energy (0.078), physiological feature AFE-based valence had nearly twice \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Factor** & **Estimate** & **SE 1** & **DF 2** & **T Value** & **\(p\) Value** & **R2 Value 3** \\ \hline AFE-based valence & 0.936 & 0.171 & 129 & 5.480 & \textless{}0001 *4 & 0.209 \\ EDA Z-Score & \(-\)0.029 & 0.030 & 129 & \(-\)0.960 & 0.337 & 0.006 \\ Phasic & 0.007 & 0.034 & 127 & 0.210 & 0.837 & \textless{}0.001 \\ Slope & \(-\)18.565 & 47.559 & 144 & \(-\)0.390 & 0.697 & 0.001 \\ Entropy & 0.031 & 0.079 & 144 & 0.400 & 0.693 & 0.001 \\ Centroid & 0.000 & 0.000 & 144 & 0.250 & 0.807 & \textless{}0.001 \\ Spread & 0.000 & 0.000 & 144 & 0.330 & 0.743 & \textless{}0.001 \\ PPE 5 & 0.000 & 0.000 & 144 & 0.420 & 0.677 & 0.001 \\ F0 Standard Deviation & \(-\)0.008 & 0.003 & 144 & \(-\)2.460 & 0.015 & 0.038 \\ F0 mean & 0.000 & 0.002 & 144 & 0.020 & 0.984 & \textless{}0.001 \\ Log energy & \(-\)0.002 & 0.011 & 144 & \(-\)0.150 & 0.884 & \textless{}0.001 \\ \hline \hline \end{tabular}  
* SE: Standard Error. 2 DF: Degree of Freedom.3 R2: R-Squared. 4 Significant factors following the Bonferroni correction, with threshold of 0.004, identified with *. 5 PPE: Pitch Period Entropy.  
\end{table} 
Table 8: Regression results of STE dimension. the strength in explaining arousal ratings. Hence, the relationship between AFE-based valence is stronger than any observed speech feature in assessing users' arousal levels. In addition to sharing a relationship with dimensions of valence and arousal, multiple linear regression revealed a statistically significant relationship between the evaluated dimension of STEE and AFE-based valence, with a _p_-value of <0.0001 (see Table 8). Speech factor F0 standard deviation also shared a relationship, with a _p_-value of 0.015. Following the Bonferroni correction, only the physiological factor AFE-based valence remained statistically significant. The R-squared value of AFE-based valence was 0.208, and 0.038 for F0 standard deviation. Consequently, physiological feature AFE-based valence was approximately five times stronger than the voice feature F0 standard deviation in explaining STEE ratings. Thus, the relationship strength of AFE-based valence and dimension STEE surpasses that of any speech feature.