  Deep learning models are becoming predominant in many fields of machine
learning. Text-to-Speech (TTS), the process of synthesizing artificial speech
from text, is no exception. To this end, a deep neural network is usually
trained using a corpus of several hours of recorded speech from a single
speaker. Trying to produce the voice of a speaker other than the one learned is
expensive and requires large effort since it is necessary to record a new
dataset and retrain the model. This is the main reason why the TTS models are
usually single speaker. The proposed approach has the goal to overcome these
limitations trying to obtain a system which is able to model a multi-speaker
acoustic space. This allows the generation of speech audio similar to the voice
of different target speakers, even if they were not observed during the
training phase.
