59 citations · 91 across the 15 of their papers we have counts for
4 papers · 1 filter
AE-Flow: AutoEncoder Normalizing Flow
Jakub Mosiński, Piotr Biliński, Thomas Merritt +2
Recently normalizing flows have been gaining traction in text-to-speech (TTS) and voice conversion (VC) due to their state-of-the-art (SOTA) performance. Normalizing flows are unsu…
Creating New Voices using Normalizing Flows
Piotr Bilinski, Thomas Merritt, Abdelhamid Ezzerg +5
Creating realistic and natural-sounding synthetic speech remains a big challenge for voice identities unseen during training. As there is growing interest in synthesizing voices of…
Comparing normalizing flows and diffusion models for prosody and acoustic modelling in text-to-speech
Guangyan Zhang, Thomas Merritt, Manuel Sam Ribeiro +10
Neural text-to-speech systems are often optimized on L1/L2 losses, which make strong assumptions about the distributions of the target data space. Aiming to improve those assumptio…
SCRAPS: Speech Contrastive Representations of Acoustic and Phonetic Spaces
Ivan Vallés-Pérez, Grzegorz Beringer, Piotr Bilinski +2
Numerous examples in the literature proved that deep learning models have the ability to work well with multimodal data. Recently, CLIP has enabled deep learning systems to learn s…