no code implementations • 19 Oct 2023 • Frederik Rautenberg, Michael Kuhlmann, Jana Wiechmann, Fritz Seebauer, Petra Wagner, Reinhold Haeb-Umbach
Unsupervised speech disentanglement aims at separating fast varying from slowly varying components of a speech signal.
no code implementations • 8 Aug 2023 • Michael Kuhlmann, Adrian Meise, Fritz Seebauer, Petra Wagner, Reinhold Haeb-Umbach
To quantify disentanglement, we identify acoustic features that are highly speaker-variant and can serve as proxies for the factors of variation underlying speech.
no code implementations • 5 Sep 2022 • Michael Kuhlmann, Fritz Seebauer, Janek Ebbers, Petra Wagner, Reinhold Haeb-Umbach
Disentangling speaker and content attributes of a speech signal into separate latent representations followed by decoding the content with an exchanged speaker representation is a popular approach for voice conversion, which can be trained with non-parallel and unlabeled speech data.