## 1 Introduction  
Speech is an incredibly powerful means of communication, as it not only codifies linguistic information, i.e., a message, but also provides paralinguistic cues about the emotional state of the speaker [1]. Emotion is, therefore, a key element for seamless human-computer interaction (HCI), both in the input channel, by means of emotional speech recognition [2] and in its output, through expressive speech synthesis [3], among others. In this context, emotions have been traditionally represented: (i) using a dimensional space, such as the circumplex model, which is defined by arousal, valence, and dominance [4]; or, (ii) as discrete categories, such as those defined in [5] and denoted as the _big six_ basic emotions, namely, anger, disgust, fear, happiness, sadness, and surprise. According to these categories, expressive speech databases are built containing spontaneous speech, elicited speech, or acted speech [2]. In order to model the specific characteristics of spoken emotions, acoustic features can be extracted from these databases and analysed to describe them with respect to neutral speech [6], which is typically considered as reference of inexpressiveness. In this regard, several studies focusing on basic emotions have found specific prosodic patterns of F0, energy and speech rate. Angry and happy speech, for instance, present higher F0, energy and speech rate values compared with neutral speech, while the opposite occurs with sadness [7]. Alternatively, the emotional content of speech can be studied by manipulating the extracted features following an analysis-by-synthesis scheme [8], as illustrated in Figure 1. To this aim, neutral speech utterances are analysed to extract features that are subsequently modified according to the analysed expressive utterances to obtain resynthesised expressive stimuli. The obtained results can be objectively and/or subjectively evaluated with respect to the non-expressive reference and to what extent they resemble the expressive target. In this regard, speech synthesis approaches that are based on the voice production theory are of particular interest since they can provide valuable insights into the mechanisms involved in the generation of emotional speech. Most of these approaches follow the so-called source-filter model [9], which considers the speech signal to be composed of an excitation (glottal pulses) amplified at certain frequencies through the vocal tract (formants). The relevance of both the glottal source and the vocal tract in the production of expressive speech has been explored in different investigations, considering a source-filter model and following an analysis-by-synthesis scheme. In [10], the authors analysed to what extent different phonation types contribute to the perception of emotion by resynthesising a set of utterances using the VocalTractLab synthesizer 2.0 [11]. This articulatory-based synthesiser is based on a 3D vocal tract geometric model and integrates a self-oscillating model of the vocal folds. It allows simulating the pressed-to-breathy phonation continuum by varying the degree of glottal abduction. In that work, the authors manually controlled the synthesiser using different gestural scores (to account for both supraglottal and laryngeal articulation) to mimic pre-recorded sentences as well as to produce certain variations of their phonation. The portrayed emotional utterances were resynthesised with their original phonation type, as well as with purely breathy, modal, and pressed phonations, which were evaluated perceptually. The obtained results showed that the recognition of fear, anger, and neutral speech demands specific phonation types, while it mainly relies on prosodic parameters for sadness, happiness, boredom, and disgust. In [12], a similar study was conducted using a Klatt formant-based synthesiser with a modified Liljencrants-Fant (LF) glottal flow model [13] as input.