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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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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)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)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)Adversarial Disentanglement of Speaker Representation for Attribute-Driven Privacy Preservation.P. Noé, M. MohammadAmini, D. Matrouf, T. Parcollet, and J. Bonastre. CoRR, (2020)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)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)SpeechBrain: A General-Purpose Speech Toolkit.M. Ravanelli, T. Parcollet, P. Plantinga, A. Rouhe, S. Cornell, L. Lugosch, C. Subakan, N. Dawalatabad, A. Heba, J. Zhong and 11 other author(s). CoRR, (2021)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.