## 1 Introduction
In more recent years, within the framework of parametric statistical synthesis, Markov models have been replaced by deep neural networks (DNNs) [12] with very good results in terms of quality of synthetic speech. DNNs can overcome some of the limitations of Gaussian models in representing the complex non-linear relationships that exist between the acoustic parameters of speech generation and the symbolic representation of speech. In [13], a very complete review of different possible strategies to use deep networks in the generation of the acoustic parameters of speech can be found. Finally, more recently, DNNs are used not only for signal generation, but to carry out the entire TTS conversion chain. Deep Voice [14] was the first of these systems in which each stage of the TTS system was implemented using neural networks. Progressively, more and more end-to-end systems were devised (Deep Voice 2 [15], Deep Voice 3 [16] and Char2 Wav [17]). Finally, totally end-to-end architectures have also been proposed, such as Tacotron [18], Tacotron 2 [19] and ClariNet [20] that generate spectrograms starting from the text. Subsequently, these spectrograms are converted into speech using WaveNet [21] or the Griffin-Lim algorithm [22]. These end-to-end systems are providing very good results in terms of the quality of the synthetic voice generated. However, the amount of data required to build a robust TTS system based on neural networks is enormous. Although experiments have been performed to adapt voices and build systems with smaller amounts of data [23], in general, it has not been possible to produce quality voices with amounts of data comparable to those used in the production of personalized voices in parametric-statistical systems. To generate custom voices using DNNs, it is possible to embed the identity of the speaker in the model so that synthetic voices are generated with the characteristics of the voice of different speakers using only a few minutes of each voice. These representations of the speaker's voice (speaker embedding) have already been successfully used to generate different voices in Tacotron-based systems [15] but they only allow generating speech for those speakers seen during training. With the goal to generate quality synthetic voices for any speaker, strategies such as VoiceLoop [24] are more appropriate: it proposes an architecture based on a fixed-size buffer that can generate voice from speakers not seen in training. Another strategy suitable to generate voices for speakers not seen during training is knowledge transfer as proposed in [25]. In any case, in these strategies, there are quality differences in the results obtainedfor speakers seen and not seen during the training [26] so that there are still important challenges to solve in order to generate the voice of any speaker. In the work described here, the synthesis is based on a parametric statistical TTS system using adaptation techniques to obtain new voices with a small amount of recordings from a new speaker [9; 27] and an average voice obtained with high quality recordings from professional speakers. This system has been implemented in a publicly available website [28], which allows users to record his or her own voice and download the resulting personalized synthetic voice to his or her device. At the same time, the synthetic voice remains in the system and is made available to orally disabled users through a 'Voice Bank' which acts as a repository of donated synthetic voices. The whole process is unsupervised and fully automated, which leads to a wide variability in the final quality of the synthetic voices. Indeed, the quality of the synthetic voice will strongly depend on the personal traits of the speaker or voice donor (speaking speed variability, degree of articulation, existence of a pathology in his or her voice, age, smoking habits, etc.). Another important factor is the similarity of the speaker's regional accent to that of the average voice.