## 3 Experiments  
This section presents the main elements of the conducted experiments, that is, the considered emotional speech database, the GlottDNN-based inverse filtering features, and the objective and subjective evaluations. The expressive data used in the experiments were extracted from a parallel emotional Spanish speech database recorded by a professional female speaker (acted speech), downsampled to 16 kHz [40], and subsequently labelled for expressive text-to-speech speech synthesis purposes [41]. Among the five speaking styles composing the expressive database, three of them were chosen for this work. Specifically, 1250 neutral, happy, and aggressive paired short utterances were retrieved from the database, thus, covering modal and tense phonation styles, respectively. These utterances--with an average of 1.2 words per utterance--ensure a phonetic coverage of the Spanish language. Out of them, 1035 ut  
Figure 2: Block diagram of the analysis-by-synthesis methodology designed to evaluate the contributions of vocal tract and glottal source spectral cues (VT and GSS) in the generation of voice-expressive (EXP) vowels. \(M\) pairs of neutral and expressive utterances from a parallel speech corpus are parameterised with the GlottDNN vocoder, and aligned through dynamic time warping (DTW). Next, the features of each neutral utterance \(\text{ut}_{\text{N}}^{\dagger}\) (marked with \({}^{\prime}\)) are time-scaled according to the DTW alignment, and transplanted with the \(\text{FO}_{\text{E}}\) and \(\text{EN}_{\text{E}}\) of the expressive pair \(\text{ut}_{\text{E}}^{\dagger}\) counterpart. The final step is GlottDNN-based synthesis, which generates different expressive output utterances by applying \(\text{GSS}_{\text{X}}\) and \(\text{VT}_{\text{Y}}\) to their vowels—being \(X,Y\) either neutral (N) or expressive (E). (**a**) Overall diagram of the analysis-by-synthesis methodology. (**b**) Details of the GlottDNN analysis. terances were used in this work, specifically those containing at least one of the vowels \([\mathrm{a}]\), \([\mathrm{i}]\) or \([\mathrm{u}]\), as they represent the three extreme vowels of the Spanish vowel triangle. As a result, a total of 1852 paired vowels were considered in the experiments. Neutral vowels had an F0 mean of 152 Hz, while the happy and aggressive vowels presented higher values, of 268 Hz and 257 Hz, respectively. Regarding the GlottDNN-based analysis-by-synthesis approach, the default settings of the GlottDNN ([[https://github.com/ljuvela/GlottDNN](https://github.com/ljuvela/GlottDNN)]([https://github.com/ljuvela/GlottDNN](https://github.com/ljuvela/GlottDNN)), accessed on 25 October 2021) were considered. That is, the GSS and the VT cues were parameterised using 10 and 30 LSF coefficients per frame, respectively, considering frames of 25 ms and 15 ms in length for voiced and unvoiced frames, accordingly. The GlottDNN pulse generation model was trained using the whole neutral corpus (of 2.4 h in length) following [21]. For the GlottDNN-based synthesis stage, after transplanting the target expressive prosody in its neutral pairs, the contributions of GSS and VT cues to the production of each target emotion were evaluated by considering four different configurations, denoted as \(\mathrm{GSS_{N}VT_{N}}\) (the baseline configuration), \(\mathrm{GSS_{E}VT_{N}}\), \(\mathrm{GSS_{N}VT_{E}}\), and \(\mathrm{GSS_{E}VT_{E}}\) (the expressive target configuration). The contributions of GSS and VT cues to the generation of happy and aggressive expressive styles were evaluated through both objective measures and subjective tests. The former was determined by computing the spectral distances between the neutral-expressive vowel pairs, considering the expressive vowel as the target reference. To that effect, GSS and VT cues were obtained from the neutral vowel (\(\mathrm{GSS_{N}}\), \(\mathrm{VT_{N}}\)), and from the expressive vowel (\(\mathrm{GSS_{E}}\), \(\mathrm{VT_{E}}\)).