## 3 Experiments
Specifically, the similarity between the \(\mathrm{GSS_{N}}\) and the \(\mathrm{GSS_{E}}\) was computed using the Itakura-Saito LPC-based spectral distance denoted as \(d_{\mathrm{IS}}(\mathrm{GSS_{N}},\mathrm{GSS_{E}})\); a metric that was also used to compare the VT cues, that is, \(d_{\mathrm{IS}}(\mathrm{VT_{N}},\mathrm{VT_{E}})\). To that effect, the GlottDNN parameterises GSS and VT cues as LSF vectors, at a frame level, from which LSF vectors at the vowel level were obtained using the median to reduce coarticulation effects. Finally, LSFs were translated into LPC to compute the Itakura-Saito distances [42]. Moreover, the similarities of each version of the \([\mathrm{a}]\), \([\mathrm{i}]\) and \([\mathrm{u}]\) vowels obtained from one of the three possible configurations with respect to the expressive target configuration were measured as the symmetrical Kullback-Leibler spectral distance [43] between their long term average spectra (LTAS) and the corresponding one of their expressive target pairs, i.e., \(d_{\mathrm{KL}}(\mathrm{GSS_{X}VT_{Y}},\mathrm{GSS_{E}VT_{E}})\). To do so, LTAS were computed as the Welch's power spectral estimate, considering a 15-ms Hamming window with 50% overlap and a 2048-point FFT [30]. Finally, a multiple stimuli with hidden reference and anchor (MUSHRA) test [44] was conducted to evaluate the contribution of GSS and VT cues to the perception of the two target emotions through the four aforementioned synthesis configurations on the six vowels under study (\(\Psi=\{[\mathrm{a}]\), \([\mathrm{a}]\), \([\mathrm{i}]\), \([\mathrm{i}]\), \([\mathrm{u}]\), \([\mathrm{u}]\)). To this end, a total of 24 words were selected from the speech database subset containing only one vowel from the modified set of vowels \(\Psi\) plus possibly other vowels (i.e., \([\mathrm{e}]\) and/or \([\mathrm{e}]\) and/or \([\mathrm{o}]\) and/or \([\mathrm{o}]\)). In this way, for each type of vowel from \(\Psi\) four words were obtained, in which only the respective vowel was modified in terms of GSS and/or VT cues using the aforementioned analysis-by-synthesis methodology, while the other vowels were kept with the neutral version of both GSS and VT. Four versions of the test were prepared, each one consisting of 13 evaluation sets (6 words per emotion plus one control point to validate the evaluator consistency according to the Pearson correlation coefficient \(r\)). In each set, the participants were asked to rate the perceived emotional intensity for each one of the four versions of the word on a 0 to 100 scale. A GlottDNN-based resynthesised utterance different from those evaluated was included in the test as an example of the target happy/aggressive emotion to support the listener evaluation. Twenty-five Spanish native speakers with an average age of 29.1 took one of the four versions of the online test using headphones and the Web Audio Evaluation Tool [45]. Among them, 56.0% of the participants had experience in playing and/or producing music, 32.0% in audio software/hardware design, and the 40.0% in audio or speech research. According to the results of the control sets, the responses of nine participants were discarded since they presented significant criteria inconsistencies (i.e., with \(r<0.5\)). In order to determine if the differences between the evaluated configurations were statistically significant, the Wilcoxon signed-rank test [46] was applied to both the objective and subjective results. The obtained \(p\)-values were corrected according to the Holm-Bonferroni method.