Transfer the linguistic representations from TTS to accent conversion
  with non-parallel data
Xi Chen Jiakun Pei Liumeng Xue Mingyang Zhang
Accent conversion aims to convert the accent of a source speech to a target
accent, meanwhile preserving the speaker's identity. This paper introduces a
novel non-autoregressive framework for accent conversion that learns
accent-agnostic linguistic representations and employs them to convert the
accent in the source speech. Specifically, the proposed system aligns speech
representations with linguistic representations obtained from Text-to-Speech
(TTS) systems, enabling training of the accent voice conversion model on
non-parallel data. Furthermore, we investigate the effectiveness of a
pretraining strategy on native data and different acoustic features within our
proposed framework. We conduct a comprehensive evaluation using both subjective
and objective metrics to assess the performance of our approach. The evaluation
results highlight the benefits of the pretraining strategy and the
incorporation of richer semantic features, resulting in significantly enhanced
audio quality and intelligibility.