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Contrastive predictive coding (CPC) aims to learn representations of speech by distinguishing future observations from a set of negative examples.
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E. Dunbar, R. Algayres, J. Karadayi, M. Bernard, J. Benjumea, X.-N. Cao, L. Miskic, C. Dugrain, L. Ondel, A. W. Black et al. , “The Zero Resource Speech Challenge 2019: TTS without T,” in Proc. Interspeech , 2019
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E. Dunbar, J. Karadayi, M. Bernard, X.-N. Cao, R. Algayres, L. Ondel, L. Besacier, S. Sakti, and E. Dupoux, “The zero resource speech challenge 2020: Discovering discrete subword and word units,” in Proc. Interspeech , 2020
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2021
Closest in time.
E. Dunbar, M. Bernard, N. Hamilakis, T. A. Nguyen, M. de Seyssel, P. Rozé, M. Rivière, E. Kharitonov, and E. Dupoux, “The interspeech zero resource speech challenge 2021: Spoken language modelling,” in Proc. Interspeech , 2021
2021
Closest in time.
J. Chorowski, G. Ciesielski, J. Dzikowski, A. Łańcucki, R. Marxer, M. Opala, P. Pusz, P. Rychlikowski, and M. Stypułkowski, “Information retrieval for zerospeech 2021: The submission by university of wroclaw,” in Proc. Interspeech , 2021
2021
Closest in time.
T. Maekaku, X. Chang, Y. Fujita, L.-W. Chen, S. Watanabe, and A. Rudnicky, “Speech representation learning combining conformer cpc with deep cluster for the zerospeech challenge 2021,” in Proc. Interspeech , 2021
2021
Closest in time.