This paper evaluates the effectiveness of a Cycle- GAN based voice converter (VC) on four speaker identification (SID) systems and an automated speech recognition (ASR) system for various pur- poses. Audio samples converted by the VC model are classified by the SID systems as the intended target at up to 46% top-1 accuracy among more than 250 speakers. This encouraging result in imitating the target styles led us to investigate if converted (synthetic) samples can be used to improve ASR training. Unfortunately, adding syn- thetic data to the ASR training set only marginally improves word and character error rates. Our re- sults indicate that even though VC models can successfully mimic the style of target speakers as measured by SID systems, improving ASR train- ing with synthetic data from VC systems needs further research to establish its efficacy.