In the last few years, deep learning has led to very good performance on a variety of
problems, such as visual recognition, speech recognition and natural language processing …

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 each …

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 each …

We introduce the Merlin speech synthesis toolkit for neural network-based speech synthesis.
The system takes linguistic features as input, and employs neural networks to predict …

Data-driven modelling and synthesis of motion is an active research area with applications
that include animation, games, and social robotics. This paper introduces a new class of …

Autoregressive models are among the best performing neural density estimators. We
describe an approach for increasing the flexibility of an autoregressive model, based on …

There has been a lot of recent interest in designing neural network models to estimate a
distribution from a set of examples. We introduce a simple modification for autoencoder …

Statistical parametric speech synthesis (SPSS) combines an acoustic model and a vocoder
to render speech given a text. Typically decision tree-clustered context-dependent hidden …

Recently, recurrent neural networks (RNNs) as powerful sequence models have re-emerged
as a potential acoustic model for statistical parametric speech synthesis (SPSS). The long …

Acoustic models based on long short-term memory recurrent neural networks (LSTM-RNNs)
were applied to statistical parametric speech synthesis (SPSS) and showed significant …