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Sample Efficient Adaptive Text-to-Speech
[article]
2019
arXiv
pre-print
We present a meta-learning approach for adaptive text-to-speech (TTS) with few data. During training, we learn a multi-speaker model using a shared conditional WaveNet core and independent learned embeddings for each speaker. The aim of training is not to produce a neural network with fixed weights, which is then deployed as a TTS system. Instead, the aim is to produce a network that requires few data at deployment time to rapidly adapt to new speakers. We introduce and benchmark three
arXiv:1809.10460v3
fatcat:zchuw4fbifb37ddmem2yaqvmry