  Recently, it has become easier to obtain speech data from various media such
as the internet or YouTube, but directly utilizing them to train a neural
text-to-speech (TTS) model is difficult. The proportion of clean speech is
insufficient and the remainder includes background music. Even with the global
style token (GST). Therefore, we propose the following method to successfully
train an end-to-end TTS model with limited broadcast data. First, the
background music is removed from the speech by introducing a music filter.
Second, the GST-TTS model with an auxiliary quality classifier is trained with
the filtered speech and a small amount of clean speech. In particular, the
quality classifier makes the embedding vector of the GST layer focus on
representing the speech quality (filtered or clean) of the input speech. The
experimental results verified that the proposed method synthesized much more
high-quality speech than conventional methods.
