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

Voice Cloning: a Multi-Speaker Text-to-Speech Synthesis Approach based on Transfer Learning