  We report our NTU-AISG Text-to-speech (TTS) entry systems for the Blizzard
Challenge 2020 in this paper. There are two TTS tasks in this year's challenge,
one is a Mandarin TTS task, the other is a Shanghai dialect TTS task. We have
participated both. One of the main challenges is to build TTS systems with
low-resource constraints, particularly for the case of Shanghai dialect, of
which about three hours data are available to participants. To overcome the
constraint, we adopt an average-speaker modeling method. That is, we first
employ external Mandarin data to train both End-to-end acoustic model and
WaveNet vocoder, then we use Shanghai dialect to tune the acoustic model and
WaveNet vocoder respectively. Apart from this, we have no Shanghai dialect
lexicon despite syllable transcripts are provided for the training data. Since
we are not sure if similar syllable transcripts are provided for the evaluation
data during the training stage, we use Mandarin lexicon for Shanghai dialect
instead. With the letter, as decomposed from the corresponding Mandarin
syllable, as input, though the naturalness and original speaker similarity of
the synthesized speech are good, subjective evaluation results indicate the
intelligibility of the synthesized speech is deeply undermined for the Shanghai
dialect TTS system.
