  Emotional Voice Conversion, or emotional VC, is a technique of converting
speech from one emotion state into another one, keeping the basic linguistic
information and speaker identity. Previous approaches for emotional VC need
parallel data and use dynamic time warping (DTW) method to temporally align the
source-target speech parameters. These approaches often define a minimum
generation loss as the objective function, such as L1 or L2 loss, to learn
model parameters. Recently, cycle-consistent generative adversarial networks
(CycleGAN) have been used successfully for non-parallel VC. This paper
investigates the efficacy of using CycleGAN for emotional VC tasks. Rather than
attempting to learn a mapping between parallel training data using a
frame-to-frame minimum generation loss, the CycleGAN uses two discriminators
and one classifier to guide the learning process, where the discriminators aim
to differentiate between the natural and converted speech and the classifier
aims to classify the underlying emotion from the natural and converted speech.
The training process of the CycleGAN models randomly pairs source-target speech
parameters, without any temporal alignment operation. The objective and
subjective evaluation results confirm the effectiveness of using CycleGAN
models for emotional VC. The non-parallel training for a CycleGAN indicates its
potential for non-parallel emotional VC.
