## 4 Results and Discussion
### Discussion  
Several aspects of the results obtained from the objective and subjective evaluations may be the subject of discussion. On the one hand, when only \(\text{GSS}_{\text{E}}\) is considered in the generation of the expressive vowel, the spectral distance to the target is significantly reduced for all the vowels and emotions except happy \([\text{i}]\) (see Figure 4). However, according  
Figure 5: Results of the MUSHRA perceptual test. Boxplots depict the perceived emotion intensity scores reported by the participants. The dotted lines represent the mean of the distributions, and whiskers are set to 5 th and 95 th percentiles. The statistically significant differences are marked with \({}^{**}\) (\(p<0.01\)) and \({}^{*}\) (\(p<0.05\)). to the results of the perceptual test (see Figure 5), the GSS contribution is only significant on vowels \([\mathrm{i}]+[\mathrm{i}]\) and \([\mathrm{u}]+[\mathrm{u}]\). This could be explained by looking at the differences between \(\mathrm{GSS_{N}}\) and \(\mathrm{GSS_{E}}\) (see left part of Figure 3), which are more pronounced for these vowels, especially for the aggressive ones. On the other hand, considering only \(\mathrm{VT_{E}}\) significantly reduces the spectral distances to the target in all the cases, as can be seen in Figure 4. Nevertheless, from the perceptual point of view, such differences are only significant for vowels \([\mathrm{a}]+[\mathrm{a}]\). As Figure 3 shows, these are exactly the vowels which present the largest differences between \(\mathrm{VT_{N}}\) and \(\mathrm{VT_{E}}\). The aforementioned differences between vowels can be also analysed by looking at the averaged spectra of the happy and aggressive vowels (see Figures 6 and 7, respectively). It can be observed how GSS differences are more pronounced for \([\mathrm{i}]+[\mathrm{i}]\) and \([\mathrm{u}]+[\mathrm{u}]\) than for \([\mathrm{a}]+[\mathrm{a}]\) vowels. Conversely, \(\mathrm{VT}\) spectral differences are particularly relevant in vowels \([\mathrm{a}]+[\mathrm{a}]\), where a clear shift of the first two formants can be observed. Although this work bears some resemblance to [17], there are several differences that should be pointed out. In order to study the relevance of GSS and \(\mathrm{VT}\) cues in resembling the target emotion, the contribution of prosody was minimised. To this end, the synthesised utterances were transplanted with the prosody of their expressive pairs instead of using the neutral prosody, as in [17], biasing the obtained results significantly. On the other hand, short utterances were used instead of isolated vowels. This has allowed us to study vowels  
Figure 6: Averaged spectra of the analysed configurations for the happy emotion. The first and the second column show the spectra obtained from the mean vectors of glottal source spectra and vocal tract, respectively, for the neutral (\(\mathrm{GSS_{N}}\), \(\mathrm{VT_{N}}\)) and expressive (\(\mathrm{GSS_{E}}\), \(\mathrm{VT_{E}}\)) configurations. The third column depicts the mean LTAS of the samples synthesised with the considered combinations of GSS and \(\mathrm{VT}\). in their phonetic context, and to ask for the perceived emotional intensity instead of using the arousal-valence space, as done in [17]. The present work has focused on two expressive speaking styles characterised by a tense phonation, using happy and aggressive acted speech. Further work could be done to extend the study to other acted speaking styles (e.g., sad acted speech, with more relaxed phonation types) as well as expressive speech obtained in other different conditions (e.g., real or elicited speech). Another aspect that should be addressed in future studies is the inverse filtering process. High-pitched female speech and closed vowels (with low F1) represent a challenge to this process [47]. In this regard, future advances in inverse filtering methods could be considered in order to improve the precision of the GSS and VT cue estimations.