## 2 Literature Review and Hypotheses Development
### Speech Features  
When considering implicit methods, one obvious choice for assessing changes in the affective state is through the study of acoustic characteristics known as speech features. Indeed, research has suggested the human voice to be a ubiquitous and insightful medium of vocal communication [20; 21; 22; 23; 24; 25]. In the field of emotion detection and speech research, common prosodic features such as fundamental frequency (F0) (e.g., minimum, maximum, mean, jitter) and energy (e.g., loudness, shimmer) as well as duration are often observed and considered among the most common [21; 26; 27]. Other vocal paralinguistic features, such as psychoacoustics features of speech rate, pitch changes, pitch contours, voice quality, spectral content, energy level, and articulation, are also often extracted due to their informative nature relating to emotion detection [28; 29]. Each vocal paralinguistic feature pertains to different vocal cues. For instance, F0 depicts the rate of vocal fold vibration and is perceived as vocal pitch, where the pitch period represents the fundamental period of the signal [30; 31]. Deriving from F0, pitch period entropy (PPE) is a measure that denotes the impaired control of F0 during sustained phonation [32; 33]. On the other hand, spectral slope and spread respectively represent the observed tendency to have low energy during high frequencies, and the total bandwidth of a speech signal using spectral centroid, a measure used to evaluate the brightness of a speech [34]. As for spectral entropy, it can assess silence and voice region of speech [35]. In sum, various speech features exist and denote vocal characteristics relating to states of being. A summary of the defined features may be found in Table 1 below. Studies in both HCI and non-HCI contexts have extracted numerous speech features to explain cognitive and affective states. For instance, research surrounding PPE has suggested the speech feature to be indicative of [PERSON]'s disease [32; 33]. When assessing affective states, various speech features have been used simultaneously by researchers. As seen within a study by [PERSON] et al. [36], spectral entropy, alongside spectral centroid, spectral spread, and energy, was observed in the aim of analyzing speakers' emotions. In research by [PERSON] and [PERSON] [26], 1038 emotional expressions were analyzed according to 13 prosodic acoustic parameters, including F0 and its variations. Within a HCI context, speech features have been studied through the lens of speech emotion recognition (SER) systems, in which emotional states via speech signals are analyzed [37]. In line with SER systems, emotion voice conversion is meant to generate expressive speech from neutral synthesized speech or natural human voice [38]. For example, research by [PERSON] et al. [39] analyzed F0, power envelope, and spectral sequency and duration to propose a voice conversion system for emotion that allowed for neutral speech to be transformed into emotional speech, with dimensions of valence and arousal serving as a control to the degrees of emotion. Valence refers to the degree of pleasure or displeasure, whereas arousal denotes the levels of alertness [40]. Moreover, in order to assess a system's recognition accuracy upon the Chinese emotional speech database, researchers extracted an array of speech features, including spectral centroid, spectral crest, spectral decrease, spectral entropy, spectral flatness, spectral flux, spectral kurtosis, spectral roll-off point, spectral spread, spectral slope, and spectral skewness, in addition to prosodic features of energy and pitch [41].