  Foreign accent conversion (FAC) is a special application of voice conversion
(VC) which aims to convert the accented speech of a non-native speaker to a
native-sounding speech with the same speaker identity. FAC is difficult since
the native speech from the desired non-native speaker to be used as the
training target is impossible to collect. In this work, we evaluate three
recently proposed methods for ground-truth-free FAC, where all of them aim to
harness the power of sequence-to-sequence (seq2seq) and non-parallel VC models
to properly convert the accent and control the speaker identity. Our
experimental evaluation results show that no single method was significantly
better than the others in all evaluation axes, which is in contrast to
conclusions drawn in previous studies. We also explain the effectiveness of
these methods with the training input and output of the seq2seq model and
examine the design choice of the non-parallel VC model, and show that
intelligibility measures such as word error rates do not correlate well with
subjective accentedness. Finally, our implementation is open-sourced to promote
reproducible research and help future researchers improve upon the compared
systems.
