## 5 Discussion  
The primary goal of this study was to compare the effectiveness of physiological and speech measures through their respective features in explaining the affective states of users during emotionally charged voice user interface interactions. Our research used speech and physiological measures employing EDA and facial expression analysis. As a result, we extracted eight distinct speech features, such as F0, spectral slope, and spectral spread, alongside three physiological features, being EDA z-score, phasic EDA, and AFE-based valence. Results suggest that speech features are indeed explicative of users' emotions during voice user interface interactions (H1). More precisely, relationships between the speech features of spectral slope, spectral spread, and log energy with the dimension of arousal can be noted. Of the three, log energy shared the strongest relationship strength with the arousal dimension. As suggested in speech literature, the energy of vocal responses is reflective of arousal [74]. Research regarding the subject suggests energy as well as F0 and speech rate to be the most indicative speech features of arousal, with high arousal associated with high frequency energy [63, 68, 69, 72]. Hence, our results are in line with previous research, which consequently supports H1. Contrary to what was hypothesized, within the context of this study, the amplitude of the extracted EDA features does not share a relationship with the emotional intensity of users during voice user interface interactions (H2a). Although EDA is widely considered an appropriate measure for arousal, the latency of skin conductance response is approximately two seconds, with a range between one and five seconds [96]. Considering the fact that certain questions (such as \"Really?\") were brief, the timeframe of analysis might have excluded important indicative electrodermal signals. As noted in this study and suggested within literature, arousal can manifest itself through various modalities, including facial expressions and speech [58, 59]. Enhanced arousal levels influence the intensity of facial reactions [97]. Since the observed voice user interface interactions stemmed from emotionally charged events, users' facial expressions may have been accentuated and were consequently reflective of arousal levels. Hence, the relationship between AFE-based valence and the dimension of arousal was stronger than phasic EDA and EDA z-score, both deemed statistically insignificant in relation to the observed dimension. Thus, H2a is not supported. As for the dimension of valence, the strength of the relationship between the amplitude of the extracted AFE-based valence feature and the dimension in question was approximately 41 times more powerful than the most predictive speech feature, suggesting a relationship between the extracted physiological feature and the emotional intensity of users during voice user interface interactions (H2b). This result supports previous findings in emotion literature suggesting facial expression to be more indicative of valence than speech features [76, 77]. Indeed, results correspond with the idea that facial expression analysis is one of the most reliable measures of valence, as individuals are more likely to express emotions through facial micromovements [10]. Thus, H2b is supported. On the contrary, research has suggested that there are no specific vocal cues associated with valence [65, 67, 70]. Moreover, the effects of valence are often vocally unapparent, as they are masked by other emotional dimensions such as arousal and dominance [98]. Our results are in line with the literature, as no speech feature was deemed statistically significant in explaining valence. On the contrary, with the exception of the control dimension, physiological feature AFE-based valence shared a significant relationship with all observed emotion dimensions.