3.6k citations · 3.6k across the 4 of their papers we have counts for
4 papers
Residual Adapters for Few-Shot Text-to-Speech Speaker Adaptation
Nobuyuki Morioka, Heiga Zen, Nanxin Chen +2
Adapting a neural text-to-speech (TTS) model to a target speaker typically involves fine-tuning most if not all of the parameters of a pretrained multi-speaker backbone model. Howe…
WaveFit: An Iterative and Non-autoregressive Neural Vocoder based on Fixed-Point Iteration
Yuma Koizumi, Kohei Yatabe, Heiga Zen +1
Denoising diffusion probabilistic models (DDPMs) and generative adversarial networks (GANs) are popular generative models for neural vocoders. The DDPMs and GANs can be characteriz…
Training Text-To-Speech Systems From Synthetic Data: A Practical Approach For Accent Transfer Tasks
Lev Finkelstein, Heiga Zen, Norman Casagrande +9
Transfer tasks in text-to-speech (TTS) synthesis - where one or more aspects of the speech of one set of speakers is transferred to another set of speakers that do not feature thes…
WaveNet: A Generative Model for Raw Audio
Aaron van den Oord, Sander Dieleman, Heiga Zen +6
This paper introduces WaveNet, a deep neural network for generating raw audio waveforms. The model is fully probabilistic and autoregressive, with the predictive distribution for e…