23 citations · 23 across the 3 of their papers we have counts for
3 papers
cs.LG2024★ 23 cited
BASE TTS: Lessons from building a billion-parameter Text-to-Speech model on 100K hours of data
Mateusz Łajszczak, Guillermo Cámbara, Yang Li +16
We introduce a text-to-speech (TTS) model called BASE TTS, which stands for ig daptive treamable TTS with mergent abilities. BASE TT…
eess.AS2023
Controllable Emphasis with zero data for text-to-speech
Arnaud Joly, Marco Nicolis, Ekaterina Peterova +11
We present a scalable method to produce high quality emphasis for text-to-speech (TTS) that does not require recordings or annotations. Many TTS models include a phoneme duration m…
eess.IV2023
Whole-body PET image denoising for reduced acquisition time
Ivan Kruzhilov, Stepan Kudin, Luka Vetoshkin +2
This paper evaluates the performance of supervised and unsupervised deep learning models for denoising positron emission tomography (PET) images in the presence of reduced acquisit…