  In voice conversion (VC), an approach showing promising results in the latest
voice conversion challenge (VCC) 2020 is to first use an automatic speech
recognition (ASR) model to transcribe the source speech into the underlying
linguistic contents; these are then used as input by a text-to-speech (TTS)
system to generate the converted speech. Such a paradigm, referred to as
ASR+TTS, overlooks the modeling of prosody, which plays an important role in
speech naturalness and conversion similarity. Although some researchers have
considered transferring prosodic clues from the source speech, there arises a
speaker mismatch during training and conversion. To address this issue, in this
work, we propose to directly predict prosody from the linguistic representation
in a target-speaker-dependent manner, referred to as target text prediction
(TTP). We evaluate both methods on the VCC2020 benchmark and consider different
linguistic representations. The results demonstrate the effectiveness of TTP in
both objective and subjective evaluations.
