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Recent work in spoken language modeling shows the possibility of learning a language unsupervisedly from raw audio without any text labels.
1907
Earlier work this paper cites.
2011
Earlier work this paper cites.
V. Panayotov, G. Chen, D. Povey, and S. Khudanpur, “Librispeech: An ASR corpus based on public domain audio books,” in 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , 2015, pp. 5206–5210
2015
Earlier work this paper cites.
A. Radford, K. Narasimhan et al. , “Improving language understanding by generative pre-training,” in arxiv , 2018
2018
Earlier work this paper cites.
J. Shen, R. Pang, R. J. Weiss, M. Schuster, N. Jaitly, Z. Yang, Z. Chen, Y. Zhang, Y. Wang, R. Skerrv-Ryan, R. A. Saurous, Y. Agiomvrgiannakis, and Y. Wu, “Natural TTS synthesis by conditioning wavenet on MEL spectrogram predictions,” in 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , 2018, pp. 4779–4783
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, I. Sutskever et al. , “Language models are unsupervised multitask learners,” OpenAI blog , vol. 1, no. 8, p. 9, 2019
2019
Earlier work this paper cites.
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of deep bidirectional transformers for language understanding,” in Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) . Minneapolis, Minnesota: Association for Computational Linguistics, Jun. 2019, pp. 4171–4186. [Online]. Available: https://aclanthology.org/N19-1423
2019
Earlier work this paper cites.
S. Schneider, A. Baevski, R. Collobert, and M. Auli, “wav2vec: Unsupervised pre-training for speech recognition,” in Interspeech 2019, 20th Annual Conference of the International Speech Communication Association, Graz, Austria, 15-19 September 2019 , G. Kubin and Z. Kacic, Eds. ISCA, 2019, pp. 3465–3469. [Online]. Available: https://doi.org/10.21437/Interspeech.2019-1873
2019
Cited alongside, same era.
M. Ott, S. Edunov, A. Baevski, A. Fan, S. Gross, N. Ng, D. Grangier, and M. Auli, “fairseq: A fast, extensible toolkit for sequence modeling,” in Proceedings of NAACL-HLT 2019: Demonstrations , 2019. [Online]. Available: https://aclanthology.org/N19-4009
2019
Cited alongside, same era.
A. Wang and K. Cho, “BERT has a mouth, and it must speak: BERT as a Markov random field language model,” in Proceedings of the Workshop on Methods for Optimizing and Evaluating Neural Language Generation . Minneapolis, Minnesota: Association for Computational Linguistics, Jun. 2019, pp. 30–36. [Online]. Available: https://aclanthology.org/W19-2304
2019
Cited alongside, same era.
J. Salazar, D. Liang, T. Q. Nguyen, and K. Kirchhoff, “Masked language model scoring,” in Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics . Online: Association for Computational Linguistics, Jul. 2020, pp. 2699–2712. [Online]. Available: https://aclanthology.org/2020.acl-main.240
2020
Later among the works it cites.
W.-N. Hsu, B. Bolte, Y.-H. H. Tsai, K. Lakhotia, R. Salakhutdinov, and A. Mohamed, “HuBERT: Self-supervised speech representation learning by masked prediction of hidden units,” IEEE/ACM Transactions on Audio, Speech, and Language Processing , vol. 29, pp. 3451–3460, 2021. [Online]. Available: https://ieeexplore.ieee.org/document/9585401
2021
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2021
Later among the works it cites.
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T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray, B. Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever, and D. Amodei, “Language models are few-shot learners,” in Advances in Neural Information Processing Systems , H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, and H. Lin, Eds., vol. 33. Curran Associates, Inc., 2020, pp. 1877–1901. [Online]. Available: https://proceedings.neurips.cc/paper/2020/file/1457c0d6bfcb4967418bfb8ac142f64a-Paper.pdf
2020
Cited alongside, same era.
A. Baevski, Y. Zhou, A. Mohamed, and M. Auli, “wav2vec 2.0: A framework for self-supervised learning of speech representations,” in Advances in Neural Information Processing Systems , H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, and H. Lin, Eds., vol. 33. Curran Associates, Inc., 2020, pp. 12 449–12 460. [Online]. Available: https://proceedings.neurips.cc/paper/2020/file/92d1e1eb1cd6f9fba3227870bb6d7f07-Paper.pdf
2020
Cited alongside, same era.
A. Baevski, M. Auli, and A. Mohamed, “Effectiveness of self-supervised pre-training for ASR,” in 2020 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2020, Barcelona, Spain, May 4-8, 2020 . IEEE, 2020, pp. 7694–7698. [Online]. Available: https://doi.org/10.1109/ICASSP40776.2020.9054224
2020
Cited alongside, same era.
A. Baevski, S. Schneider, and M. Auli, “vq-wav2vec: Self-supervised learning of discrete speech representations,” in 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020 . OpenReview.net, 2020. [Online]. Available: https://openreview.net/forum?id=rylwJxrYDS
2020
Cited alongside, same era.
M. Rivière, A. Joulin, P.-E. Mazaré, and E. Dupoux, “Unsupervised pretraining transfers well across languages,” in ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing . Barcelona / Virtual, Spain: IEEE, May 2020, pp. 7414–7418. [Online]. Available: https://hal.archives-ouvertes.fr/hal-02959418
2020
Cited alongside, same era.
E. Dunbar, M. Bernard, N. Hamilakis, T. A. Nguyen, M. De Seyssel, P. Rozé, M. Rivière, E. Kharitonov, and E. Dupoux, “The Zero Resource Speech Challenge 2021: Spoken language modelling,” in Interspeech 2021 - Conference of the International Speech Communication Association , Brno, Czech Republic, Aug. 2021. [Online]. Available: https://hal.inria.fr/hal-03329301
2021
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B. van Niekerk, L. Nortje, M. Baas, and H. Kamper, “Analyzing speaker information in self-supervised models to improve zero-resource speech processing,” 08 2021, pp. 1554–1558
2021
Later among the works it cites.
K. Lakhotia, E. Kharitonov, W.-N. Hsu, Y. Adi, A. Polyak, B. Bolte, T.-A. Nguyen, J. Copet, A. Baevski, A. Mohamed, and E. Dupoux, “On Generative Spoken Language Modeling from Raw Audio,” Transactions of the Association for Computational Linguistics , Feb. 2021. [Online]. Available: https://hal.inria.fr/hal-03329219
2021
Later among the works it cites.
E. Kharitonov, J. Copet, K. Lakhotia, T. A. Nguyen, P. Tomasello, A. Lee, A. Elkahky, W.-N. Hsu, A. Mohamed, E. Dupoux, and Y. Adi, “textless-lib: a library for textless spoken language processing,” 2022
2022
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