Investigating very deep highway networks for parametric speech synthesis

Investigating very deep highway networks for parametric speech synthesis

The depth of the neural network is a vital factor that affects its performance. Recently a new architecture called highway network was proposed. This network facilitates the training process of a very deep neural network by using gate units to control a information highway over the conventional hidden layer. For the speech synthesis task, we investigate the performance of highway networks with ...

Multiple feed-forward deep neural networks for statistical parametric speech synthesis

In this paper, we investigate a combination of several feedforward deep neural networks (DNNs) for a high-quality statistical parametric speech synthesis system. Recently, DNNs have significantly improved the performance of essential components in the statistical parametric speech synthesis, e.g. spectral feature extraction, acoustic modeling and spectral post-filter. In this paper our proposed...

Highway-LSTM and Recurrent Highway Networks for Speech Recognition

Recently, very deep networks, with as many as hundreds of layers, have shown great success in image classification tasks. One key component that has enabled such deep models is the use of “skip connections”, including either residual or highway connections, to alleviate the vanishing and exploding gradient problems. While these connections have been explored for speech, they have mainly been ex...

Investigating gated recurrent neural networks for speech synthesis

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 short-term memory (LSTM) architecture is particularly attractive because it addresses the vanishing gradient problem in standard RNNs, making them easier to train. Although recent studies have demonstrated that LSTMs ca...

Parametric representations for speech synthesis