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.

本文介绍了一种利用深度双向长短期记忆网络和神经合成器来进行语音的情感转换，同时使用包含丰富语言信息的音素后验概率作为辅助输入特征，提高了转换效果的多目标情感转换架构 Multi-target EVC (MTEVC)，并将条件 WaveNet 和基于流的 WaveNet (FloWaveNet) 神经合成器作为其核心部件，训练了这些神经合成器，并将语音库中的说话者信息以及情感信息定义为附加特征。实验结果的客观度量和主观评估验证了提出的 MTEVC 架构在情感语音转换方面的有效性。