  In this paper, we explore the possibility of speech synthesis from low
quality found data using only limited number of samples of target speaker. We
try to extract only the speaker embedding from found data of target speaker
unlike previous works which tries to train the entire text-to-speech system on
found data. Also, the two speaker mimicking approaches which are adaptation and
speaker-encoder-based are applied on newly released LibriTTS dataset and
previously released VCTK corpus to examine the impact of speaker variety on
clarity and target-speaker-similarity .
