  Sequence-to-sequence (seq2seq) voice conversion (VC) models have greater
potential in converting electrolaryngeal (EL) speech to normal speech (EL2SP)
compared to conventional VC models. However, EL2SP based on seq2seq VC requires
a sufficiently large amount of parallel data for the model training and it
suffers from significant performance degradation when the amount of training
data is insufficient. To address this issue, we suggest a novel, two-stage
strategy to optimize the performance on EL2SP based on seq2seq VC when a small
amount of the parallel dataset is available. In contrast to utilizing
high-quality data augmentations in previous studies, we first combine a large
amount of imperfect synthetic parallel data of EL and normal speech, with the
original dataset into VC training. Then, a second stage training is conducted
with the original parallel dataset only. The results show that the proposed
method progressively improves the performance of EL2SP based on seq2seq VC.
