  Applying changes to an input speech signal to change the perceived speaker of
speech to a target while maintaining the content of the input is a challenging
but interesting task known as Voice conversion (VC). Over the last few years,
this task has gained significant interest where most systems use data-driven
machine learning models. Doing the conversion in a low-latency real-world
scenario is even more challenging constrained by the availability of
high-quality data. Data augmentations such as pitch shifting and noise addition
are often used to increase the amount of data used for training machine
learning based models for this task. In this paper we explore the efficacy of
common data augmentation techniques for real-time voice conversion and
introduce novel techniques for data augmentation based on audio and voice
transformation effects as well. We evaluate the conversions for both male and
female target speakers using objective and subjective evaluation methodologies.
