  We work to create a multilingual speech synthesis system which can generate
speech with the proper accent while retaining the characteristics of an
individual voice. This is challenging to do because it is expensive to obtain
bilingual training data in multiple languages, and the lack of such data
results in strong correlations that entangle speaker, language, and accent,
resulting in poor transfer capabilities. To overcome this, we present a
multilingual, multiaccented, multispeaker speech synthesis model based on
RADTTS with explicit control over accent, language, speaker and fine-grained
$F_0$ and energy features. Our proposed model does not rely on bilingual
training data. We demonstrate an ability to control synthesized accent for any
speaker in an open-source dataset comprising of 7 accents. Human subjective
evaluation demonstrates that our model can better retain a speaker's voice and
accent quality than controlled baselines while synthesizing fluent speech in
all target languages and accents in our dataset.
