{"id":25492,"date":"2021-11-09T14:02:04","date_gmt":"2021-11-09T13:02:04","guid":{"rendered":"https:\/\/lium.univ-lemans.fr\/?p=25492"},"modified":"2021-11-23T18:05:05","modified_gmt":"2021-11-23T17:05:05","slug":"non-parametric-subspace-models-for-acoustic-units-discovery","status":"publish","type":"post","link":"https:\/\/lium.univ-lemans.fr\/en\/non-parametric-subspace-models-for-acoustic-units-discovery\/","title":{"rendered":"Non-Parametric Subspace Models for Acoustic Units Discovery"},"content":{"rendered":"

Seminar from Lucas Ondel, post-doc at l’Universit\u00e9 Paris-Saclay<\/h2>\n

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Date:<\/strong> 12\/11\/2021
\nTime:<\/strong> 11h30
\nLocalization:<\/strong> IC2, Boardroom, online<\/a>
\nSpeaker:<\/strong> Lucas Ondel<\/a><\/p>\n

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Non-Parametric Subspace Models for Acoustic Units Discovery <\/strong><\/p>\n

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This talk will be about subspace non-parametric models for the task of learning a set of acoustic units from unlabeled speech recordings. I will show how we can leverage phonetically transcribed data to learn the notion of phone (encoded as a phonetic subspace) and then “project” data from other languages into this subspace to learn their phonetics. I will show that the Bayesian (non-parametric) framework is a natural candidate to implement this idea and it clearly outperforms other neural network baselines such as vq-wav2vec and vq-vae.<\/p><\/div><\/div><\/div><\/div>

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 <\/div><\/div><\/div>","protected":false},"excerpt":{"rendered":"

Seminar from Lucas Ondel, post-doc at l’Universit\u00e9 Paris-Saclay   Date: 12\/11\/2021 Time: 11h30 Localization: IC2, Boardroom, online Speaker: Lucas Ondel   Non-Parametric Subspace Models for Acoustic Units Discovery   This talk will be about subspace non-parametric models for the task of learning a set of acoustic units from unlabeled speech recordings. I will show how […]<\/p>\n

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