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.

该研究提出了一种新颖的非自回归框架用于口音转换，通过学习口音无关的语言表示并使用它们来转换源语音中的口音，从而保留说话者的身份。研究还调查了在我们提出的框架中的本地数据和不同声学特征的预训练策略的有效性，并使用主客观度量来全面评估了我们方法的性能。评估结果突出了预训练策略和丰富的语义特征的益处，显著提高了音频质量和可理解性。

使用非平行数据将从 TTS 中转移的语言表示进行口音转换

Transfer the linguistic representations from TTS to accent conversion  with non-parallel data

Most people who have tried to learn a foreign language would have experienced
difficulties understanding or speaking with a native speaker's accent. For
native speakers, understanding or speaking a new accent is likewise a difficult
task. An accent conversion system that changes a speaker's accent but preserves
that speaker's voice identity, such as timbre and pitch, has the potential for
a range of applications, such as communication, language learning, and
entertainment. Existing accent conversion models tend to change the speaker
identity and accent at the same time. Here, we use adversarial learning to
disentangle accent dependent features while retaining other acoustic
characteristics. What sets our work apart from existing accent conversion
models is the capability to convert an unseen speaker's utterance to multiple
accents while preserving its original voice identity. Subjective evaluations
show that our model generates audio that sound closer to the target accent and
like the original speaker.

研究使用对抗学习来实现口音转换，能够将说话者的声音身份保留下来，并可将未知说话者的话语转换为多种口音，主观评估显示该模型生成更接近目标口音且类似于原说话者的音频。