Search Results for author: Trevor Wood

Found 4 papers, 1 papers with code

Distribution augmentation for low-resource expressive text-to-speech

no code implementations13 Feb 2022 Mateusz Lajszczak, Animesh Prasad, Arent van Korlaar, Bajibabu Bollepalli, Antonio Bonafonte, Arnaud Joly, Marco Nicolis, Alexis Moinet, Thomas Drugman, Trevor Wood, Elena Sokolova

This paper presents a novel data augmentation technique for text-to-speech (TTS), that allows to generate new (text, audio) training examples without requiring any additional data.

Data Augmentation

Discrete Acoustic Space for an Efficient Sampling in Neural Text-To-Speech

no code implementations24 Oct 2021 Marek Strelec, Jonas Rohnke, Antonio Bonafonte, Mateusz Łajszczak, Trevor Wood

We present a Split Vector Quantized Variational Autoencoder (SVQ-VAE) architecture using a split vector quantizer for NTTS, as an enhancement to the well-known Variational Autoencoder (VAE) and Vector Quantized Variational Autoencoder (VQ-VAE) architectures.

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