  In this paper, we introduce an emotional speech synthesizer based on the
recent end-to-end neural model, named Tacotron. Despite its benefits, we found
that the original Tacotron suffers from the exposure bias problem and
irregularity of the attention alignment. Later, we address the problem by
utilization of context vector and residual connection at recurrent neural
networks (RNNs). Our experiments showed that the model could successfully train
and generate speech for given emotion labels.
