Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Grzegorz Beringer"'
Autor:
Magdalena Proszewska, Grzegorz Beringer, Daniel Sáez-Trigueros, Thomas Merritt, Abdelhamid Ezzerg, Roberto Barra-Chicote
In this paper, we propose GlowVC: a multilingual multi-speaker flow-based model for language-independent text-free voice conversion. We build on Glow-TTS, which provides an architecture that enables use of linguistic features during training without
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::75de51f0fc0118eade3ba985f57e6130
http://arxiv.org/abs/2207.01454
http://arxiv.org/abs/2207.01454
Publikováno v:
Interspeech 2021.
Text-to-speech systems recently achieved almost indistinguishable quality from human speech. However, the prosody of those systems is generally flatter than natural speech, producing samples with low expressiveness. Disentanglement of speaker id and
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e1d19fd06dcf5573b5303fcf03cfca9e
https://doi.org/10.21437/interspeech.2021-562
https://doi.org/10.21437/interspeech.2021-562
Autor:
Szymon Zaporowski, Thomas Drugman, Alicja Serafinowicz, Roberto Barra-Chicote, Daniel Korzekwa, Jaime Lorenzo-Trueba, Jasha Droppo, Bozena Kostek, Grzegorz Beringer
This paper describes two novel complementary techniques that improve the detection of lexical stress errors in non-native (L2) English speech: attention-based feature extraction and data augmentation based on Neural Text-To-Speech (TTS). In a classic
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::af9bcb769ca963dd187acb2c432d1268
http://arxiv.org/abs/2012.14788
http://arxiv.org/abs/2012.14788
Publikováno v:
FedCSIS
In recent years, word embeddings have been shown to improve the performance in NLP tasks such as syntactic parsing or sentiment analysis. While useful, they are problematic in representing ambiguous words with multiple meanings, since they keep a sin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b6f91dd755325b0497eda9fbaf758f6c
https://doi.org/10.15439/2019f120
https://doi.org/10.15439/2019f120