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Cassia Valentini-Botinhao
Researcher at University of Edinburgh
Publications - 58
Citations - 1573
Cassia Valentini-Botinhao is an academic researcher from University of Edinburgh. The author has contributed to research in topics: Speech synthesis & Intelligibility (communication). The author has an hindex of 18, co-authored 53 publications receiving 1223 citations.
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Proceedings ArticleDOI
Investigating RNN-based speech enhancement methods for noise-robust Text-to-Speech
TL;DR: Experiments show that the a-layer can effectively learn to interpolate the acoustic features between speakers, and tackle the problem of speaker interpolation by adding a new output layer (a-layer) on top of the multi-output branches.
Proceedings ArticleDOI
Deep neural networks employing Multi-Task Learning and stacked bottleneck features for speech synthesis
TL;DR: It is shown that the hidden representation used within a DNN can be improved through the use of Multi-Task Learning, and that stacking multiple frames of hidden layer activations (stacked bottleneck features) also leads to improvements.
Journal ArticleDOI
Evaluating the intelligibility benefit of speech modifications in known noise conditions
Martin Cooke,Catherine Mayo,Cassia Valentini-Botinhao,Yannis Stylianou,Bastian Sauert,Yan Tang +5 more
TL;DR: The current study compares the benefits of speech modification algorithms in a large-scale speech intelligibility evaluation and quantifies the equivalent intensity change, defined as the amount in decibels that unmodified speech would need to be adjusted by in order to achieve the same intelligibility as modified speech.
Proceedings ArticleDOI
Speech Enhancement for a Noise-Robust Text-to-Speech Synthesis System using Deep Recurrent Neural Networks
TL;DR: The use of a recurrent neural network to enhance acoustic parameters prior to training showed that the voice built with enhanced parameters was ranked significantly higher than the ones trained with noisy speech and speech that has been enhanced using a conventional enhancement system.