## 5 Discussion
### Theoretical Contributions  
As a result of this paper, five theoretical contributions can be noted. For one, current research regarding voice user interface evaluation gravitates around explicit methods, such as interviews, observations, diaries, and questionnaires [13; 99; 100]. Data obtained from explicit measures relying on self-reported measures can be flawed, as users are at risk of cognitive and retrospective biases [9]. By including implicit measures, our study avoids such biases while taking into account real-time, subconscious reactions linked to important emotional states [9]. Consequently, results from this study contribute to the understanding of underlying emotions lived by users interacting with voice user interfaces. Hence, the measures used to capture the emotional responses provoked by voice user interface interactions are both informative and complementary to the current literature. Secondly, few studies have observed the users' speech features during voice user interface interactions, and less have done so in combination with physiological measures, as research within the study of emotion through speech tends to focus on single sensor data [101]. Thus, utilizing multiple physiological measures within this field of research is a rare occurrence. Recording multiple physiological measures further provides a more thorough understanding of the underlying emotions lived by users during such events, while allowing for the comparative strength of each measure's extracted feature in explaining emotional responses induced by voice user interfaces to be assessed. By isolating each measure, this study further confirms the indicative nature of speech and physiological features in assessing users' emotional responses, as suggested in previous emotion-centered research. Indeed, extracted physiological feature AFE-based valence and speech features such as spectral spread, log energy, and F0 were indicative of the observed emotion dimensions. The relationship strength of these features in regard to assessing user emotions is in line with previous research [26; 34; 36; 43]. Thirdly, an important contribution of this study relates to the nuances of each measure's strength in explaining four distinct emotional dimensions, as it allowed for their effectiveness to be compared. As stressed previously, the effectiveness of physiological feature AFE-based valence surpasses all extracted features of both physiological and vocal nature. Indeed, its statistical relationship to valence, arousal, and STEE dimensions is significant and dominant. Hence, results from this study contribute to the understanding of measurement effectiveness in assessing user emotions during voice user interface interactions. Fourthly, in addition to exploring the dimensions of valence and arousal, this study considered control as an additional emotional dimension. Within speech literature, the dimension of control has received less attention in comparison to its counterparts of valence and arousal [102]. Thus, this study further contributes to the literature by observing this dimension. Unlike the valence and arousal dimensions, results suggest that the control dimension is best explained by the speech feature of spectral slope. Indeed, spectral slope had approximately three times more strength than extracted physiological feature EDA z-score in explaining control ratings. However, this relationship is the weakest amongst the observed dimensions, as the R-squared value was below 5%. Moreover, it was not considered statistically significant. Previous speech-emotion studies assessing the control dimension have been inconsistent. Result variances in F0, speech rate, and voice intensity have been noted [73]. Indeed, when observing the dimension of control in relation to spectral slope, research by [PERSON] et al. [69] suggest that low dominance is accompanied by a flatter spectral slope, contrary to results obtained by [PERSON] and [PERSON] [103].