## 1 Introduction
A set of target sentences were copy-synthesised and subsequently resynthesised with different phonation types (breathy, creaky, falsetto, modal, and tense) through a rule-based model that controlled the glottal source model parameters. A forced choice perceptual test was conducted, in which the participants were asked to assign each stimulus to either neutral, anger, joy, sadness, frightened, or bored speech. The phonation types were distinguished by the listeners and lead to a satisfactory emotional impression. In [14], a similar synthesis scheme was considered to explore the interaction of voice quality and F0 contours with the listener's language background (Irish, English, Russian, Spanish, and Japanese) in the perception of affect. Three types of synthetic stimuli were generated from a short Swedish utterance spoken with modal voice: (i) stimuli varied in voice quality, (ii) modal stimuli incorporating  
Figure 1: Workflow diagram of the analysis-by-synthesis approach applied to expressive speech. Neutral speech utterances are analysed to extract features that are manipulated according to the analysed expressive utterances to obtain resynthesised expressive stimuli. The results are objectively and/or subjectively evaluated in comparison with the non-expressive reference and the expressive target. affect-related F0 contours, and (iii) stimuli combining certain non-modal voice qualities with the affect-related F0 contours from (ii). A perceptual test was conducted asking participants to rate the stimuli on six bipolar scales defined with contrastive adjectives at each end: indignant-apologetic, interested-bored, formal-intimate, stressed-relaxed, happy-sad, and fearless-scared. The results suggested that stimuli incorporating non-modal voice qualities, with or without F0 variation, were generally more effective in conveying emotions than stimuli only varying F0. In [15], the \(R_{d}\) glottal shape parameter [16] was used to control the LF model so as to produce expressive voice along the tense-lax continuum, exploring its influence on the perception of emotions. Moreover, the relative importance of glottal source and vocal tract on emotional vowel perception was examined in [17] using an auto-regressive exogenous LF model. Glottal source and vocal tract parameters were estimated from isolated vowels \([\mathrm{a}]\) recorded by two professional male actors using four emotions (neutral, joy, anger, and sadness). Besides the eight resynthesised original vowels, sixteen synthetic vowels per speaker were obtained by combining the averaged glottal source parameters from one emotion and the averaged vocal tract parameters from another emotion. The synthesised vowels were perceptually evaluated using a valence-arousal model. The glottal source was found to have a dominant role, mainly due to the predominant effect of the prosodic features, i.e., F0, intensity, and duration. These cues were subsequently _neutralised_ in a second experiment using the values from the neutral vowels. As a consequence, the relevance of the vocal tract was higher, but the resulting vowels were perceived in a similar way to their neutral counterparts, negatively affecting the expressiveness analysis. The aforementioned approaches have focused on the analysis and resynthesis of a small set of isolated vowels or short utterances at most, involving typically costly manual tuning processes when developing the analysis-and-resynthesis approach. However, recent advances in inverse filtering have shown promising results in the automatic decomposition of the speech signal into its glottal source and vocal tract components [18]. Among these techniques, the glottal flow model-based vocoder allows real-time voice manipulations, such as shifting vowel formants and modifying voice quality through the manipulation of the glottal source model [19]. The GlottHMM approach, based on hidden Markov models, showed good results for the analysis of expressive nuances, beyond prosodic parameters such as F0 and rhythm [20].