Sample Efficient Adaptive Text-to-Speech
international conference on learning representations, 2019.
These results are robust across speech datasets recorded under different conditions and, we demonstrate that the generated samples are capable of confusing the state-of-the-art text-independent speaker verification system
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....More
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