  This paper proposes architectures that facilitate the extrapolation of
emotional expressions in deep neural network (DNN)-based text-to-speech (TTS).
In this study, the meaning of "extrapolate emotional expressions" is to borrow
emotional expressions from others, and the collection of emotional speech
uttered by target speakers is unnecessary. Although a DNN has potential power
to construct DNN-based TTS with emotional expressions and some DNN-based TTS
systems have demonstrated satisfactory performances in the expression of the
diversity of human speech, it is necessary and troublesome to collect emotional
speech uttered by target speakers. To solve this issue, we propose
architectures to separately train the speaker feature and the emotional feature
and to synthesize speech with any combined quality of speakers and emotions.
The architectures are parallel model (PM), serial model (SM), auxiliary input
model (AIM), and hybrid models (PM&AIM and SM&AIM). These models are trained
through emotional speech uttered by few speakers and neutral speech uttered by
many speakers. Objective evaluations demonstrate that the performances in the
open-emotion test provide insufficient information. They make a comparison with
those in the closed-emotion test, but each speaker has their own manner of
expressing emotion. However, subjective evaluation results indicate that the
proposed models could convey emotional information to some extent. Notably, the
PM can correctly convey sad and joyful emotions at a rate of >60%.
