6 citations · 9 across the 8 of their papers we have counts for
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eess.AS2023
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…
cs.CV2023★ 1 cited
Leveraging triplet loss for unsupervised action segmentation
E. Bueno-Benito, B. Tura, M. Dimiccoli
In this paper, we propose a novel fully unsupervised framework that learns action representations suitable for the action segmentation task from the single input video itself, with…