## 1 Introduction  
Speech is the most natural method that humans use to communicate with each other. When, due to an accident or illness, one person loses the ability to speak, technology can provide solutions to mitigate the impact of his or her disability. Text-to-speech (TTS) systems are a fundamental component of the so-called alternative and augmentative communication (AAC) devices, providing a synthetic voice to speak aloud the text that has been introduced through some kind of input device, such as a keyboard or an eye-gaze-controlled device. TTS systems have been available in the market for many years already, and nowadays synthetic voices are not only intelligible, but also have a high level of naturalness. However, it is often the case that the offered synthetic voices do not suit the user's preferences in terms of age, accent or even gender. Commercial voices are in general obtained from professional speakers, chosen precisely because of his or her pleasant voice, neutral accent and ability to keep a homogeneous speech style during long recording sessions, conditions which greatly help to obtain a high quality synthetic voice. Consequently, there are few (and for many languages, none) commercially available voices corresponding to an old person or to a child, or voices with regional accents. The orally disabled user must then use a synthetic voice unmatched to his or her own speaking characteristics. Speech is a fundamental part of the identity of a person. Synthetic voice customization tries to keep those hints of personality, nonexistent in a generic or commercial synthetic voice. Studies such as [1] show our tendency to form an impression on the personality of other people from their voice (as happens with other features, such as the face, or the color of the skin). Other studies demonstrate that using personalized voices can facilitate intellectual development for children with vision shortages [2]. Thus, it is our belief that the use of personalized speech can help in reducing the social impact of using an electronic device for everyday communication. Until the beginning of the last decade, the technologies applied for voice generation included concatenative synthesis with unit selection [3] and parametric statistical synthesis based on Markov models [4]. Concatenative unit selection systems generate voice by concatenating prerecorded fragments of a natural voice, chosen according to sophisticated selection criteria that include acoustic, phonetic, prosodic and linguistic aspects. These techniques produce very natural results in restricted application domains, but by expanding the domain, they obtain results of variable quality [5]. In addition, they have high memory, storage and processing requirements and reduced or null flexibility to generate new voices. Statistical-parametric systems create synthetic speech from averaged models of acoustically similar speech units. Speech is transformed into a series of parameters by means of a vocoder [6]. Typically, several types of parameters are used: spectral parameters related to the spectral envelope that include information about the energy in the different frequency bands; the fundamental frequency which is related to intonation; and finally some parameters related to the degree of voicing of the source, such as aperiodicities by bands (STRAIGHT [7] and WORLD [8]) or the maximum voiced frequency (AhoCoder [9]). Among the advantages of statistical-parametric systems are their consistency, flexibility, intelligibility and low need for storage. They allow the generation of new voices, using adaptation or interpolation techniques [10], and they produce a smooth voice of stable quality, although the use of vocoders reduces its naturalness. Its intelligibility is similar to that of natural speech and even better in noisy environments [11]. In short, these systems are very well suited to be used in the context of personalized TTS.