The NTU-AISG Text-to-speech System for Blizzard Challenge 2020
This addresses the problem of low-resource TTS for specific dialects, but it is incremental as it adapts existing methods to new data.
The paper tackles building text-to-speech systems for Mandarin and Shanghai dialect under low-resource constraints, particularly with only about three hours of data for Shanghai dialect, resulting in good naturalness and speaker similarity but significantly reduced intelligibility for Shanghai dialect.
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.