## 1 Introduction  
The history of interface design has primarily revolved around Graphical User Interfaces (GUI), resulting in longstanding and familiar frameworks [1]. From [PERSON]'s 10 usability heuristics to [PERSON]'s ergonomic criteria for the evaluation of human-computer interfaces, designers have an array of tools to guide them in their conception of optimal digital experiences [2, 3]. With the rise of non-visual user interaction (No-UI), it may be argued that the groundwork for vocal interface design is still in development due to the recency and rapid growth of vocal interface technologies. Indeed, in 2020, 4.2 billion digital voice assistants worldwide were in use [4]. By 2024, this number is projected to reach 8.4 billion, a number greater than the world's population [4]. With this said, a set of validated voice user interface heuristics and guiding principles has yet to breakthrough. Research within the field has recently tried to address this matter. For example, [PERSON] et al. [5] developed an adapted version of [PERSON]'s ergonomic criteria to vocal interfaces. On the other hand, [PERSON] and [PERSON] [6] presented descriptive frameworks to quantitatively measure voice UX. Both studies relied on extensive reviews of academic and professional guidelines to propose an adapted set of criteria. These studies have contributed to the emerging field of voice user interface design, a discipline in need of support to guide designers in the conceptualization and evaluation of speech-based products. Despite this development, [PERSON] and [PERSON] [6] highlighted the fact that numerous studies in relation to voice UX rely heavily on self-reported measures, defined by the users' own report of their states of being. According to the authors, self-reported measures, based on psychometric scales, were widespread and consequently called for the development of measures, such as behavioral measures, to support findings. Self-reported measures fall within the realm of explicit measures, characterized by the conscious behaviour of subjects under scrutiny. Explicit measures, such as self-reported measures, are often adopted due to their inexpensive nature [7]. However, they are limiting, as they do not delve into the real-time automatic and subconscious reactions of users. As a result, UX professionals are at risk of overlooking key insights regarding the underlying emotions of users. Moreover, the limiting nature of certain explicit methods are made evident when evaluating voice user interfaces, notably the think-aloud method [8]. Due to the nature of this method, in which users verbally share their thoughts during interface usage, vocal interference may hamper the user's experience. To obtain a thorough understanding of the user's lived experiences, implicit measures can be used to observe emotional reactions [9]. As opposed to explicit measures, implicit measures tap into the subconscious behavior of users and can be reflected in speech or physiology. In the study of emotions during voice user interface interactions, the measure of speech is an obvious implicit measurement target due to the vocal nature of the interaction. The data obtained from this measure can be analyzed under various lenses through extracted speech features, including, for instance, pitch and fundamental frequency. However, physiological measures such as electrodermal activity (EDA) and facial micro expressions and their respective features (e.g., phasic EDA and the valence of facial micro expressions, respectively) have the potential to be equally revealing of emotional events [10,11]. Studies regarding emotions induced by voice user interface interactions seldom study both speech and physiological features simultaneously. Indeed, voice user interface evaluation often employs explicit methods, such as questionnaires, diaries, interviews, and observations [12, 13, 14, 15, 16], and do not additionally utilize implicit measures.