Simon King

Investigating gated recurrent neural networks for speech synthesis

2016 · arXiv (Cornell University)

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 …

Measuring the Cognitive Load of Synthetic Speech Using a Dual Task Paradigm

2018

We present a methodology for measuring the cognitive load (listening effort) of synthetic speech using a dual task paradigm. Cognitive load is calculated from changes in a listener’s performance on a secondary task (e.g., reaction …

Investigating gated recurrent networks for speech synthesis

2016

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 …

Deep neural networks employing Multi-Task Learning and stacked bottleneck features for speech synthesis

2015

Deep neural networks (DNNs) use a cascade of hidden representations to enable the learning of complex mappings from input to output features. They are able to learn the complex mapping from text-based linguistic features to …

Merlin: An Open Source Neural Network Speech Synthesis System

2016

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 acoustic features, which are then passed to a vocoder to …