  Expressive neural text-to-speech (TTS) systems incorporate a style encoder to
learn a latent embedding as the style information. However, this embedding
process may encode redundant textual information. This phenomenon is called
content leakage. Researchers have attempted to resolve this problem by adding
an ASR or other auxiliary supervision loss functions. In this study, we propose
an unsupervised method called the "information sieve" to reduce the effect of
content leakage in prosody transfer. The rationale of this approach is that the
style encoder can be forced to focus on style information rather than on
textual information contained in the reference speech by a well-designed
downsample-upsample filter, i.e., the extracted style embeddings can be
downsampled at a certain interval and then upsampled by duplication.
Furthermore, we used instance normalization in convolution layers to help the
system learn a better latent style space. Objective metrics such as the
significantly lower word error rate (WER) demonstrate the effectiveness of this
model in mitigating content leakage. Listening tests indicate that the model
retains its prosody transferability compared with the baseline models such as
the original GST-Tacotron and ASR-guided Tacotron.
