# Special Issue on IberSPEECH 2022: Speech and Language Technologies for Iberian Languages
## Contributions
### Speech Synthesis  
Voice cloning techniques aim to generate synthetic speech that resembles the voice of a particular speaker. The article by [PERSON] et al. (contribution 4) reflects on the relevance of considering the most appropriate input speech corpora from the target speaker as a key factor of the voice cloning process when applying deep learning. The approach focuses on selecting the most-suitable corpus subset considering the lower heterogeneity in terms of phonetic coverage and utterance speed, as well as the lower signal-to-noise ratio, as a means of obtaining cloned speech with higher synthetic quality. This quality is measured by computing two estimators of the well-known Mean Opinion Score (MOS), named NISQA and MOSnet, avoiding the need of including human perceptual evaluations in the selection process. The paper also presents an algorithm to calculate the sentence alignment of the synthesized audio at the character level in the process. The experiments consider both low and high-quality speech corpora, being the latter used as a reference, for evaluating the proposal on the Tacotron-2 text-to-speech voice-cloning framework. The results suggest that introducing a robust pre-training step when developing voice-cloning based on deep learning can significantly reduce the amount of data needed for training. It is worth mentioning that the authors found that training a voice-cloning model with only 3 h of speech data (even in a different language) from a pre-trained model resulted in similar synthetic quality to training the voice-cloning approach from scratch with larger datasets, thus, making the training process of this kind of techniques based on deep learning more efficient.  
From a different perspective of those approaches considering deep learning techniques, speech can be also generated from articulatory-based models. Recent advances on the production of voice through 3D-based vocal tract geometries using numerical simulations have been able to generate speech signals such as vowels and diphotongs, among other short utterances. For techniques based on the source-filter model, the estimation of both the glottal source and the vocal tract transfer function becomes instrumental, being particularly challenging when trying to improve expressiveness of current approaches.  
In the work conducted by [PERSON] et al. (contribution 7), the authors compare the performance of current state-of-the-art glottal inverse filtering (GIF) techniques, devoted to decompose the glottal source from the vocal tract, on a common reference dataset. The performance of different variants of iterative adaptive inverse filtering (IAIF) and quasi closed phase (QCP) approaches are implemented and evaluated on OPENGLOT's Repository I [6] extended with female vowels. Several standard GIF error metrics are computed to evaluate the obtained glottal flow signals. As a result of the conducted benchmarking on both male and female synthetic vowels with different phonation types and F0s, the authors argue that QCP-based approaches outperform their IAIF-based alternatives for almost all error metrics and evaluated scenarios, besides behaving more stable across sex (male-female), phonation type (from lax to tense: whispery, breathy, normal, and creaky), F0 values (from 100 to 360 Hz with steps of 20 Hz), and vowels. Moreover, the results show that applying GIF on female vowels is more challenging than on male vowels, presenting a general decrease in main errors when moving from tense to lax phonations.