## 1 Introduction  
Emotion expression and perception is a very important issue in human interactions and is one of the bases upon which the communication between humans is established. Therefore, the automatic detection of emotions by a computer has become a very attractive topic due to its impact on the effort towards more natural and empathic human-machine interaction systems. Emotions can be expressed in different ways, including facial expression, speech, gestures, etc. In this work, we focus on speech and its ability to provide diverse information. In addition to the message communicated, speech signals can provide information related to different aspects of the speaker. In fact, speech signals can give insights into the emotional state of the speaker or even their baseline mood, as shown in many studies about this issue [1, 2]. The probability of suffering from a disease, such as depression, Alzheimer's disease [3, 4, 5], or even COVID-19 [6], can also be extracted from speech. However, speech may also be influenced by several other variables, such as the speaker's habits, personality, culture, or specific objective [7, 8]. Human-human interactions take place in specific contexts where, to some extent, people know each other. However, current artificial agents have little capacity to imitate a real user, resulting in shallow interactions [9]. In fact, users find it hard to interact with agents with rudimentary visual and speech capacities [10]. The literature suggests that human behavior in human-human interactions is guided by the other human's behavior and is, thus, reactionary behavior [11]. However, comparisons between these two scenarios have almost only been carried out at the interaction and dialogue levels [9, 11]. The emotional exchange in both scenarios is completely different due the rudimentary emotional capacity of the agent, which results in very subtle emotions. This work aims to contrast the similarities and differences for emotions identified in two very different scenarios: human-human interactions on Spanish TV debates (TV Debates) and human-machine interactions with a virtual agent developed by the H2020 EMPATHIC project ([[http://www.empathic-project.eu](http://www.empathic-project.eu)]([http://www.empathic-project.eu](http://www.empathic-project.eu)), accessed on 3 February 2022) (Empathic VA), also in Spanish. Thus, we profile the task in each scenario or, more specifically, the speakers involved in each task. Although they are quite different, they both share the spontaneity of speech, as well as the spontaneity of the expression of emotions in real scenarios [12]. Disfluencies or spontaneous speech events, such as filled pauses or speech repairs, enrich spontaneous communication [13] with paralinguistic information that depends on the context, on the speaker profile, and on emotional state. In recent years, research on spontaneously expressed emotions in everyday tasks has gained interest in the scientific community [14,15]. However, this research has typically been conducted on emotions simulated by professional actors in artificially designed databases such as EMODB [16] or IEMOCAP [17]. The six basic emotions defined by [PERSON] [18] (anger, surprise, disgust, enjoyment, fear, and sadness) can be represented by facial expressions that typically characterize these emotions and thus can be used in the automatic identification of emotions on a face [19]. However, spontaneous emotions are more varied and complex. Furthermore, emotions expressed during acting or during a real-life scenario show significant differences [20]. In fact, only a small set of complex and compound emotions [21] can be found in real scenarios [2,15,22], and this subset is strongly dependent on the situation. Therefore, a set of categories including the emotions that arise in each specific task has to be defined. To this end, some perception experiments have to be conducted to specify the set of emotions of interest. However, this process is expensive; time consuming; and sometimes, not viable.