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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 7 of 7 1 LeBenchmark: A Reproducible Framework for Assessing Self-Supervised Representation Learning from Speech Evain, Solène; Nguyen, Ha; Le, Hang... In: INTERSPEECH 2021: Conference of the International Speech Communication Association ; https://hal.archives-ouvertes.fr/hal-03317730 ; INTERSPEECH 2021: Conference of the International Speech Communication Association, Aug 2021, Brno, Czech Republic (2021) BASE Show details 2 LeBenchmark: A Reproducible Framework for Assessing Self-Supervised Representation Learning from Speech Evain, Solène; Nguyen, Ha; Le, Hang... In: INTERSPEECH 2021: ; INTERSPEECH 2021: Conference of the International Speech Communication Association ; https://hal.archives-ouvertes.fr/hal-03317730 ; INTERSPEECH 2021: Conference of the International Speech Communication Association, Aug 2021, Brno, Czech Republic (2021) BASE Show details 3 LeBenchmark: A Reproducible Framework for Assessing Self-Supervised Representation Learning from Speech Evain, Solène; Nguyen, Ha; Le, Hang... In: INTERSPEECH 2021: ; INTERSPEECH 2021: Conference of the International Speech Communication Association ; https://hal.archives-ouvertes.fr/hal-03317730 ; INTERSPEECH 2021: Conference of the International Speech Communication Association, Aug 2021, Brno, Czech Republic (2021) BASE Show details 4 FlauBERT: Unsupervised Language Model Pre-training for French Le, Hang; Vial, Loïc; Frej, Jibril... In: Proceedings of the 12th Language Resources and Evaluation Conference ; LREC ; https://hal.archives-ouvertes.fr/hal-02890258 ; LREC, 2020, Marseille, France (2020) BASE Show details 5 FlauBERT : Unsupervised Language Model Pre-training for French ; FlauBERT : des modèles de langue contextualisés pré-entraînés pour le français Le, Hang; Vial, Loïc; Frej, Jibril... In: Actes de la 6e conférence conjointe Journées d'Études sur la Parole (JEP, 33e édition), Traitement Automatique des Langues Naturelles (TALN, 27e édition), Rencontre des Étudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (RÉCITAL, 22e édition). Volume 2 : Traitement Automatique des Langues Naturelles ; 6e conférence conjointe Journées d'Études sur la Parole (JEP, 33e édition), Traitement Automatique des Langues Naturelles (TALN, 27e édition), Rencontre des Étudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (RÉCITAL, 22e édition). Volume 2 : Traitement Automatique des Langues Naturelles ; https://hal.archives-ouvertes.fr/hal-02784776 ; 6e conférence conjointe Journées d'Études sur la Parole (JEP, 33e édition), Traitement Automatique des Langues Naturelles (TALN, 27e édition), Rencontre des Étudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (RÉCITAL, 22e édition). Volume 2 : Traitement Automatique des Langues Naturelles, Jun 2020, Nancy, France. pp.268-278 (2020) BASE Show details 6 Unsupervised Word Segmentation: does tone matter ? Godard, Pierre; Löser, Kevin; Allauzen, Alexandre; Besacier, Laurent; Yvon, François In: International Conference on Intelligent Text Processing and Computational Linguistics ; https://hal.archives-ouvertes.fr/hal-01910756 ; International Conference on Intelligent Text Processing and Computational Linguistics, Mar 2018, Hanoï, Vietnam (2018) Abstract: In this paper, we investigate the usefulness of tonal features for unsupervised word discovery, taking Mboshi, a low-resource tonal language from the Bantu family, as our main target language. In a preliminary step, we show that tone annotation improves the performance of \emph{supervised learning} when using a simplified representation of the data. To leverage this information in an unsupervised setting, we then present a probabilistic model based on a hierarchical Pitman-Yor process that incorporates tonal representations in its backoff structure. We compare our model with a tone-agnostic baseline and analyze if and how tone helps unsupervised segmentation on our small dataset. Keyword: [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL]; [INFO]Computer Science [cs]; Tonal Language; Unsupervised Morphology; Unwritten Language; Word discovery URL: https://hal.archives-ouvertes.fr/hal-01910756/file/Godard18doestone.pdf https://hal.archives-ouvertes.fr/hal-01910756/document https://hal.archives-ouvertes.fr/hal-01910756 BASE Hide details 7 Preliminary Experiments on Unsupervised Word Discovery in Mboshi Godard, Pierre; Adda, Gilles; Adda-Decker, Martine... In: Interspeech 2016 proceedings ; Interspeech 2016 ; https://hal.archives-ouvertes.fr/hal-01350119 ; Interspeech 2016, Sep 2016, San-Francisco, United States (2016) BASE Show details

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