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pro vyhledávání: '"Zaïdi, Julian"'
Discovering a lexicon from unlabeled audio is a longstanding challenge for zero-resource speech processing. One approach is to search for frequently occurring patterns in speech. We revisit this idea with DUSTED: Discrete Unit Spoken-TErm Discovery.
Externí odkaz:
http://arxiv.org/abs/2408.14390
Autor:
van Niekerk, Benjamin, Carbonneau, Marc-André, Zaïdi, Julian, Baas, Mathew, Seuté, Hugo, Kamper, Herman
The goal of voice conversion is to transform source speech into a target voice, keeping the content unchanged. In this paper, we focus on self-supervised representation learning for voice conversion. Specifically, we compare discrete and soft speech
Externí odkaz:
http://arxiv.org/abs/2111.02392
Publikováno v:
Proc. Interspeech (2022) 4591-4595
This paper presents Daft-Exprt, a multi-speaker acoustic model advancing the state-of-the-art for cross-speaker prosody transfer on any text. This is one of the most challenging, and rarely directly addressed, task in speech synthesis, especially for
Externí odkaz:
http://arxiv.org/abs/2108.02271
Learning to disentangle and represent factors of variation in data is an important problem in AI. While many advances have been made to learn these representations, it is still unclear how to quantify disentanglement. While several metrics exist, lit
Externí odkaz:
http://arxiv.org/abs/2012.09276
Akademický článek
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