## 6 Conclusions  
The evaluation of voice user interface experiences is an emerging topic that is gaining ground as voice recognition technology continues to grow. The study presented herein sought to understand the emotional responses experienced by users during voice user interface interactions by observing and comparing the effectiveness of physiological and speech measures through their respective features. Our results depict a stronger correlation between the emotional dimensions and physiological measures in comparison to speech. More precisely, extracted physiological feature AFE-based valence best explained user emotions. To sum up, the use of physiological measures can equip UX professionals with rich data regarding the emotional experiences lived by users during voice user interface interactions, which may contribute to the design of optimal experiences. Our study is limited by the fact that it was conducted remotely. The instructions regarding the pose of sensors and the upload of the data to the cloud were provided by an experience moderator. However, the acts were ultimately committed by the participants. Hence, a lack of control and on-sight supervision might have played a role in the technical difficulties resulting in data loss. To counter these drawbacks, future studies should consider an in-person data collection. Moreover, our experiment was limited by the use of a Wizard of Oz technique, in which the moderator played sequential MP3 recordings uploaded to a Google slides presentation. Occasional recordings were accidentally played out of order or with a significant time-lapse in between them, which resulted in a less authentic interaction in comparison to that of an actual voice user interface. Hence, future studies featuring an authentic and functional voice user interface system should be considered. Furthermore, the scope of the present study was limited in that the speech features analyzed were not exhaustive. Further studies regarding the matter should consider other speech features in order to further explore the subject. On that note, different physiological measures and their respective features should also be included to pursue the study of user emotions during voice user interface interactions. Moreover, within the context of this study,the majority of emotional events investigated were related to negative user emotions, such as frustration. Future studies should consider a diversified set of emotions, both of positive and negative nature, in order to obtain a more holistic representation. Lastly, recorded EDA data during brief voice user interface interjections was considered for the analysis. The time points of concise and occasional one-worded questions may have affected the results regarding the relationship between the extracted EDA features in relation to a users' emotional intensity during voice user interface interactions. Considering the latency of skin conductance response, ranging between one and five seconds [96], in conjunction to the time points chosen, indicative electrodermal signals might have been excluded. Future research should either consider changing the dialogue to limit brief questions or include the participant's response within the time window of EDA analysis. Conceptualization, D.S., J.B., S.S. and P.-M.L. ; methodology, D.S., J.B., S.S. and P.-M.L. ; validation, J.B., S.S. and P.-M.L. ; formal analysis, D.S. and J.B.; investigation, D.S. and J.B.; resources, P.-M.L., S.S. and J.B.; data curation, D.S., J.B., P.-M.L. and S.S.; writing--original draft preparation, D.S. ; writing--review and editing, P.-M.L., J.B. and S.S.; supervision, J.B., P.-M.L. and S.S.; project administration, J.B.; funding acquisition, P.-M.L., S.S. and R.P. All authors have read and agreed to the published version of the manuscript. This research was funded by the NSERC Industrial Research Chair 505259-16 Leger grant and by Deloitte Digital.