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LeBenchmark: A Reproducible Framework for Assessing Self-Supervised Representation Learning from Speech.

S. Evain, H. Nguyen, H. Le, M. Boito, S. Mdhaffar, S. Alisamir, Z. Tong, N. Tomashenko, M. Dinarelli, T. Parcollet, A. Allauzen, Y. Estève, B. Lecouteux, F. Portet, S. Rossato, F. Ringeval, D. Schwab, and L. Besacier. Interspeech, page 1439-1443. ISCA, (2021)

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Adversarial Disentanglement of Speaker Representation for Attribute-Driven Privacy Preservation.P. Noé, M. MohammadAmini, D. Matrouf, T. Parcollet, and J. Bonastre. CoRR, (2020)Quaternion Convolutional Neural Networks For Theme Identification Of Telephone Conversations.T. Parcollet, M. Morchid, G. Linarès, and R. Mori. SLT, page 685-691. IEEE, (2018)Quaternion Recurrent Neural Networks.T. Parcollet, M. Ravanelli, M. Morchid, G. Linarès, C. Trabelsi, R. Mori, and Y. Bengio. ICLR (Poster), OpenReview.net, (2019)Quaternion Convolutional Neural Networks for Heterogeneous Image Processing.T. Parcollet, M. Morchid, and G. Linarès. ICASSP, page 8514-8518. IEEE, (2019)ZeroFL: Efficient On-Device Training for Federated Learning with Local Sparsity.X. Qiu, J. Fernández-Marqués, P. de Gusmao, Y. Gao, T. Parcollet, and N. Lane. ICLR, OpenReview.net, (2022)Task Agnostic and Task Specific Self-Supervised Learning from Speech with LeBenchmark.S. Evain, H. Nguyen, H. Le, M. Boito, S. Mdhaffar, S. Alisamir, Z. Tong, N. Tomashenko, M. Dinarelli, T. Parcollet and 8 other author(s). NeurIPS Datasets and Benchmarks, (2021)Real-Time Personalised Speech Enhancement Transformers with Dynamic Cross-attended Speaker Representations.S. Zhang, M. Chadwick, A. Ramos, T. Parcollet, R. van Dalen, and S. Bhattacharya. INTERSPEECH, page 804-808. ISCA, (2023)Réseaux de neurones convolutifs de quaternions pour l'identification de thèmes de conversations téléphoniques.T. Parcollet, M. Morchid, and G. Linarès. CORIA, ARIA, (2019)Real to H-Space Autoencoders for Theme Identification in Telephone Conversations.T. Parcollet, M. Morchid, X. Bost, G. Linarès, and R. Mori. IEEE ACM Trans. Audio Speech Lang. Process., (2020)E2E-SINCNET: Toward Fully End-To-End Speech Recognition.T. Parcollet, M. Morchid, and G. Linarès. ICASSP, page 7714-7718. IEEE, (2020)

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LeBenchmark: A Reproducible Framework for Assessing Self-Supervised Representation Learning from Speech.

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copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML LeBenchmark: A Reproducible Framework for Assessing Self-Supervised Representation Learning from Speech.

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LeBenchmark: A Reproducible Framework for Assessing Self-Supervised Representation Learning from Speech.