  Abstract End-to-end text-to-speech (TTS) systems has proved its great success
in the presence of a large amount of high-quality training data recorded in
anechoic room with high-quality microphone. Another approach is to use
available source of found data like radio broadcast news. We aim to optimize
the naturalness of TTS system on the found data using a novel data processing
method. The data processing method includes 1) utterance selection and 2)
prosodic punctuation insertion to prepare training data which can optimize the
naturalness of TTS systems. We showed that using the processing data method, an
end-to-end TTS achieved a mean opinion score (MOS) of 4.1 compared to 4.3 of
natural speech. We showed that the punctuation insertion contributed the most
to the result. To facilitate the research and development of TTS systems, we
distributed the processed data of one speaker at
https://forms.gle/6Hk5YkqgDxAaC2BU6.
