  Recent advances in synthetic speech quality have enabled us to train
text-to-speech (TTS) systems by using synthetic corpora. However, merely
increasing the amount of synthetic data is not always advantageous for
improving training efficiency. Our aim in this study is to selectively choose
synthetic data that are beneficial to the training process. In the proposed
method, we first adopt a variational autoencoder whose posterior distribution
is utilized to extract latent features representing acoustic similarity between
the recorded and synthetic corpora. By using those learned features, we then
train a ranking support vector machine (RankSVM) that is well known for
effectively ranking relative attributes among binary classes. By setting the
recorded and synthetic ones as two opposite classes, RankSVM is used to
determine how the synthesized speech is acoustically similar to the recorded
data. Then, synthetic TTS data, whose distribution is close to the recorded
data, are selected from large-scale synthetic corpora. By using these data for
retraining the TTS model, the synthetic quality can be significantly improved.
Objective and subjective evaluation results show the superiority of the
proposed method over the conventional methods.
