  Nowadays, neural vocoders can generate very high-fidelity speech when a bunch
of training data is available. Although a speaker-dependent (SD) vocoder
usually outperforms a speaker-independent (SI) vocoder, it is impractical to
collect a large amount of data of a specific target speaker for most real-world
applications. To tackle the problem of limited target data, a data augmentation
method based on speaker representation and similarity measurement of speaker
verification is proposed in this paper. The proposed method selects utterances
that have similar speaker identity to the target speaker from an external
corpus, and then combines the selected utterances with the limited target data
for SD vocoder adaptation. The evaluation results show that, compared with the
vocoder adapted using only limited target data, the vocoder adapted using
augmented data improves both the quality and similarity of synthesized speech.
