  Text-to-Speech (TTS) synthesis plays an important role in human-computer
interaction. Currently, most TTS technologies focus on the naturalness of
speech, namely,making the speeches sound like humans. However, the key tasks of
the expression of emotion and the speaker identity are ignored, which limits
the application scenarios of TTS synthesis technology. To make the synthesized
speech more realistic and expand the application scenarios, we propose a
multi-task anthropomorphic speech synthesis framework (MASS), which can
synthesize speeches from text with specified emotion and speaker identity. The
MASS framework consists of a base TTS module and two novel voice conversion
modules: the emotional voice conversion module and the speaker voice conversion
module. We propose deep emotion voice conversion model (DEVC) and deep speaker
voice conversion model (DSVC) based on convolution residual networks. It solves
the problem of feature loss during voice conversion. The model trainings are
independent of parallel datasets, and are capable of many-to-many voice
conversion. In the emotional voice conversion, speaker voice conversion
experiments, as well as the multi-task speech synthesis experiments,
experimental results show DEVC and DSVC convert speech effectively. The
quantitative and qualitative evaluation results of multi-task speech synthesis
experiments show MASS can effectively synthesis speech with specified text,
emotion and speaker identity.
