  Neural text-to-speech (TTS) approaches generally require a huge number of
high quality speech data, which makes it difficult to obtain such a dataset
with extra emotion labels. In this paper, we propose a novel approach for
emotional TTS synthesis on a TTS dataset without emotion labels. Specifically,
our proposed method consists of a cross-domain speech emotion recognition (SER)
model and an emotional TTS model. Firstly, we train the cross-domain SER model
on both SER and TTS datasets. Then, we use emotion labels on the TTS dataset
predicted by the trained SER model to build an auxiliary SER task and jointly
train it with the TTS model. Experimental results show that our proposed method
can generate speech with the specified emotional expressiveness and nearly no
hindering on the speech quality.
