## 1 Introduction
More recently, GlottDNN [21] a successor of GlottHMM based on deep neural networks, has been used to to embed the Lombard effect on regular speech [22], obtaining good results. The source-filter models has traditionally considered a representation of the vocal tract in one dimension (1D) [23]. Although the 1D-based approach has been widely used in the literature (see e.g., [11; 24; 25]), the simplification it entails makes it only capable of modelling the propagation of plane waves along the vocal tract midline, thus, limiting the accuracy of 1D-based approaches up to about 4-5 kHz [26]. During the last decade, and thanks to the significant increase in computing power, several attempts have been developed to overcome this limitation by means of three-dimensional (3D) vocal tract models [27; 28; 29], which allow the propagation of higher order modes [26]. This characteristic is especially relevant for the production of vowels with a tense phonation [30]. So far, such models have been already used for the synthesis of vowels [29], diphthongs [31] and vowel-consonant-vowel sequences [32], including sibilants [33] that entail considering aeroacoustic sources [34], as well as works focused on tuning the vocal tract resonances [35] to be able to simulate effects such as the so-called singing formant [36]. In order to introduce some expressiveness in the 3D numerical synthesis of vowels with a tense phonation, a GlottDNN-based analysis of the spectral tilt of the glottal source was proposed in [37]. To that effect, the \(R_{d}\) parameter of an LF model was modified accordingly, and the results were evaluated on isolated aggressive and happy \([\mathrm{a}]\) vowels. Although that work showed interesting preliminary results, it was only focused on modelling one parameter of the excitation. Therefore, further investigations are still necessary to be able to generate natural expressive utterances within a 3D-based finite element methods synthesis scheme [38]. In order to advance in the modelling and integration of tense-expressive styles within a speech synthesizer based on a source-filter model, this work analyses the contribution of vocal tract (VT) and glottal source spectral (GSS) cues in the generation of happy and aggressive speech styles. To this aim, an GlottDNN-based analysis-by-synthesis approach is proposed and evaluated using a parallel Spanish speech corpus composed of neutral and expressive utterances. The contributions of GSS and/or VT cues on each target emotion are analysed through objective and perceptual tests by considering neutral utterances transplanted with the prosody (baseline), and GSS and/or VT cues from their expressive pairs vowels \([\mathrm{a}]\), \([\mathrm{i}]\) and \([\mathrm{u}]\), differentiating if they are stressed or not. The paper is organised as follows. Section 2 presents the proposed methodology based on the GlottDNN analysis and synthesis of expressive utterances to study the contribution of GSS and VT cues to the generation of happy and aggressive tense emotional vowels from neutral speech. Next, Section 3 describes the conducted experiments, whose results are detailed and discussed in Section 4. Finally, the derived conclusions from this work, as well as future research lines, are presented in Section 5.