## 2 Literature Review and Hypotheses Development
### Combination of Speech and Physiological Measures  
Emotion is often expressed through several modalities [57]. For instance, the arousal of emotion can manifest itself in speech, facial expressions, brain dynamics, and numerous peripheral physiological signals, such as heart rate variability, respiration, and, of course, electrodermal activity [58; 59]. Indeed, research has suggested that EDA dynamics are strongly influenced by respiration and speech activity [54]. With this said, a link is to be made between the study of EDA and speech features in assessing emotional behavior. Current literature regarding the study of emotions includes multi-modal research utilizing both EDA and speech features. For example, in a study by [PERSON] et al. [59], a multi-modal approach combining EDA and speech analyses was used to develop a personalized emotion recognition system allowing for the arousal levels of participants to be assessed while reading emotional words. As suggested within the study, the algorithm's performance accuracy was at its highest when combining both implicit measures, rather than observing EDA and speech features separately, as both the sympathetic activity induced by the voice and related respiration variations were captured. Within the same vein, research by [PERSON] et al. [60] proposed a speech activity detection system using the speech feature extraction technique Mel Frequency Cepstral Coefficients (MFCC) in addition to EDA. By including EDA, the system was able to perform in noisy environments and compensate for the presence of emotional conditions. Hence, the complimentary nature of both measures in explaining emotional behaviour is to be noted. On the other hand, speech features have also been studied in parallel to facial expressions. Speech and facial expressions provide a comprehensible view into a user's reaction, as visual and auditory modalities may infer a user's emotional state [61]. To assess users' emotional states in naturalistic video sequences, a study by [PERSON] et al. [61] combined information from both facial expression recognition and speech prosody feature extraction. A study by [PERSON] et al. [57] went a step further by including body gesture modality to build a multimodal emotion recognition system used to assess eight emotional states that were equally distributed in valence-arousal space. Similarly to [PERSON] et al. 's study [59], the classifiers based on both speech data and facial expressions outperformed classifiers trained with a single modality. This was also the case in research by [PERSON] et al. [62], where a multimodal system including both facial expression and emotional speech was more accurate in emotion recognition in comparison to isolated functions. A summary of the multi-method studies is found in the Table 2 below. With this said, EDA, facial micro expressions, and speech are capable of explaining emotional states, both in isolation and in conjunction with each other. As stressed, facial expressions and electrodermal activity are indicative of a user's valence and arousal levels, making them pertinent measures to the study of emotions. This leads us to our following replication hypotheses:  
**Hypothesis 2a (H2a). **_There is a relationship between the amplitude of the extracted EDA features and the emotional intensity of users during voice user interface interactions._