Pretraining Strategies, Waveform Model Choice, and Acoustic Configurations for Multi-Speaker End-to-End Speech Synthesis
By Erica Cooper, Yi Zhao, Yusuke Yasuda, Junichi Yamagishi. 2020
3 citations recorded by the source corpus.
We explore pretraining strategies including choice of base corpus with the aim of choosing the best strategy for zero-shot multi-speaker end-to-end synthesis. We also examine choice of neural vocoder for waveform synthesis, as well as acoustic configurations used for mel spectrograms and final audio output. We find that fine-tuning a multi-speaker model from found audiobook data that has passed a simple quality threshold can improve naturalness and similarity to unseen target speakers of synthetic speech. Additionally, we find that listeners can discern between a 16kHz and 24kHz sampling rate, and that WaveRNN produces output waveforms of a comparable quality to WaveNet, with a faster inference time.
Subfields: Artificial Intelligence, Signal Processing
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Citations in the corpus
- A Survey on Neural Speech Synthesis (2021, methodology): “Techniques Data Work Self-supervised Training Unpaired text or speech [51, 368, 433, 78, 140, 346, 197, 352, 71] Cross-lingual Transfer Paired text and speech [42, 390, 12, 60, 271, 105] Cross-speaker Transfer Paired text and speech [216, 125, 59, 39] Speech chain/Back transformation Unpaired text o”
- Multi-Singer: Fast Multi-Singer Singing Voice Vocoder With A Large-Scale Corpus (2021, unknown): “Researchers [8] used to perform the warmstart strategy to overcome data shortage.”