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。