Les auto-encodeurs variationnels dynamiques et leur application à la modélisation de spectrogrammes de parole

Autor: Girin, Laurent, Bie, Xiaoyu, Leglaive, Simon, Hueber, Thomas, Alameda-Pineda, Xavier

Publikováno v: JEP 2022-34e Journées d’Études sur la Parole
JEP 2022-34e Journées d’Études sur la Parole, Université de Nantes, Jun 2022, Noirmoutier, France. pp.655-663, ⟨10.21437/JEP.2022-69⟩

International audience; The Variational Autoencoder (VAE) is a powerful deep generative model that is now extensively used to represent high-dimensional complex data via a low-dimensional latent space learned in an unsupervised manner. In the origina

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https://hal.science/hal-03978396/document

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A Benchmark of Dynamical Variational Autoencoders Applied to Speech Spectrogram Modeling

Autor: Xiaoyu Bie, Laurent Girin, Thomas Hueber, Simon Leglaive, Xavier Alameda-Pineda

Publikováno v: Interspeech 2021
Interspeech 2021-22nd Annual Conference of the International Speech Communication Association
Interspeech 2021-22nd Annual Conference of the International Speech Communication Association, Aug 2021, Brno, Czech Republic. pp.46-50, ⟨10.21437/Interspeech.2021-256⟩
Interspeech 2021-22nd Annual Conference of the International Speech Communication Association, Aug 2021, Brno, Czech Republic. pp.1-5

The Variational Autoencoder (VAE) is a powerful deep generative model that is now extensively used to represent high-dimensional complex data via a low-dimensional latent space learned in an unsupervised manner. In the original VAE model, input data

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4a8a884cd5ede495f1c501cb2fe822af