  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 purposes. 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 synthetic
data to the ASR training set only marginally improves word and character error
rates. Our results indicate that even though VC models can successfully mimic
the style of target speakers as measured by SID systems, improving ASR training
with synthetic data from VC systems needs further research to establish its
efficacy.
