  Emotional voice conversion (EVC) is one way to generate expressive synthetic
speech. Previous approaches mainly focused on modeling one-to-one mapping,
i.e., conversion from one emotional state to another emotional state, with
Mel-cepstral vocoders. In this paper, we investigate building a multi-target
EVC (MTEVC) architecture, which combines a deep bidirectional long-short term
memory (DBLSTM)-based conversion model and a neural vocoder. Phonetic
posteriorgrams (PPGs) containing rich linguistic information are incorporated
into the conversion model as auxiliary input features, which boost the
conversion performance. To leverage the advantages of the newly emerged neural
vocoders, we investigate the conditional WaveNet and flow-based WaveNet
(FloWaveNet) as speech generators. The vocoders take in additional speaker
information and emotion information as auxiliary features and are trained with
a multi-speaker and multi-emotion speech corpus. Objective metrics and
subjective evaluation of the experimental results verify the efficacy of the
proposed MTEVC architecture for EVC.
