  The goal of accent conversion (AC) is to convert the accent of speech into
the target accent while preserving the content and speaker identity. AC enables
a variety of applications, such as language learning, speech content creation,
and data augmentation. Previous methods rely on reference utterances in the
inference phase or are unable to preserve speaker identity. To address these
issues, we propose a zero-shot reference-free accent conversion method, which
is able to convert unseen speakers' utterances into a target accent. Pseudo
Siamese Disentanglement Network (PSDN) is proposed to disentangle the accent
from the content representation. Experimental results show that our model
generates speech samples with much higher accentedness than the input and
comparable naturalness, on two-way conversion including foreign-to-native and
native-to-foreign.
