Fetching the paper…
Reading the bibliography…
Pseudo-labeling has recently shown promise in end-to-end automatic speech recognition (ASR).
L. Lamel, J.-L. Gauvain, and G. Adda, “Lightly supervised and unsupervised acoustic model training,” Computer Speech & Language , vol. 16, no. 1, pp. 115–129, 2002
2002
Earlier work this paper cites.
M. Mohri, F. Pereira, and M. Riley, “Weighted finite-state transducers in speech recognition,” Computer Speech & Language , vol. 16, no. 1, pp. 69–88, 2002
2002
Earlier work this paper cites.
F. Wessel and H. Ney, “Unsupervised training of acoustic models for large vocabulary continuous speech recognition,” IEEE Transactions on Speech and Audio Processing , vol. 13, no. 1, pp. 23–31, 2004
2004
Earlier work this paper cites.
A. Graves, S. Fernández, F. Gomez, and J. Schmidhuber, “Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks,” in Proceedings of the 23rd international conference on Machine learning , 2006, pp. 369–376
2006
Earlier work this paper cites.
J. Ma and R. Schwartz, “Unsupervised versus supervised training of acoustic models,” in Ninth Annual Conference of the International Speech Communication Association , 2008
2008
Earlier work this paper cites.
K. Yu, M. Gales, L. Wang, and P. C. Woodland, “Unsupervised training and directed manual transcription for lvcsr,” Speech Communication , vol. 52, no. 7-8, pp. 652–663, 2010
2010
Earlier work this paper cites.
K. Heafield, “Kenlm: Faster and smaller language model queries,” in Proceedings of the sixth workshop on statistical machine translation . Association for Computational Linguistics, 2011, pp. 187–197
2011
Earlier work this paper cites.
J. Duchi, E. Hazan, and Y. Singer, “Adaptive subgradient methods for online learning and stochastic optimization,” Journal of machine learning research , vol. 12, no. Jul, pp. 2121–2159, 2011
2011
Earlier work this paper cites.
H. Liao, E. McDermott, and A. Senior, “Large scale deep neural network acoustic modeling with semi-supervised training data for youtube video transcription,” in 2013 IEEE Workshop on Automatic Speech Recognition and Understanding . IEEE, 2013, pp. 368–373
2013
Earlier work this paper cites.
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: a simple way to prevent neural networks from overfitting,” The journal of machine learning research , vol. 15, no. 1, pp. 1929–1958, 2014
2014
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) . IEEE, 2015, pp. 5206–5210
2015
Earlier work this paper cites.
V. Manohar, D. Povey, and S. Khudanpur, “Semi-supervised maximum mutual information training of deep neural network acoustic models,” in Sixteenth Annual Conference of the International Speech Communication Association , 2015
2015
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, “Attention is all you need,” in Advances in neural information processing systems , 2017, pp. 5998–6008
2017
Cited alongside, same era.
2018
Cited alongside, same era.
2018
Cited alongside, same era.
T. Kudo and J. Richardson, “Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing,” in Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations , 2018, pp. 66–71
2018
Cited alongside, same era.
Y. Wang, A. Mohamed, D. Le, C. Liu, A. Xiao, J. Mahadeokar, H. Huang, A. Tjandra, X. Zhang, F. Zhang et al. , “Transformer-based acoustic modeling for hybrid speech recognition,” in ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2020, pp. 6874–6878
2020
Closest in time.
J. Kahn, M. Rivière, W. Zheng, E. Kharitonov, Q. Xu, P.-E. Mazaré, J. Karadayi, V. Liptchinsky, R. Collobert, C. Fuegen et al. , “Libri-light: A benchmark for asr with limited or no supervision,” in ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2020, pp. 7669–7673
2020
Closest in time.
S. Ling, Y. Liu, J. Salazar, and K. Kirchhoff, “Deep contextualized acoustic representations for semi-supervised speech recognition,” in ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2020, pp. 6429–6433
2020
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2019
Cited alongside, same era.
2019
Cited alongside, same era.
S. Karita, N. Chen, T. Hayashi, T. Hori, H. Inaguma, Z. Jiang, M. Someki, N. E. Y. Soplin, R. Yamamoto, X. Wang et al. , “A comparative study on transformer vs rnn in speech applications,” in 2019 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU) . IEEE, 2019, pp. 449–456
2019
Cited alongside, same era.
K. J. Han, R. Prieto, and T. Ma, “State-of-the-art speech recognition using multi-stream self-attention with dilated 1d convolutions,” in 2019 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU) . IEEE, 2019, pp. 54–61
2019
Cited alongside, same era.
2019
Cited alongside, same era.
A. H. Liu, H.-y. Lee, and L.-s. Lee, “Adversarial training of end-to-end speech recognition using a criticizing language model,” in ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2019, pp. 6176–6180
2019
Cited alongside, same era.
2019
Cited alongside, same era.
D. S. Park, W. Chan, Y. Zhang, C.-C. Chiu, B. Zoph, E. D. Cubuk, and Q. V. Le, “Specaugment: A simple data augmentation method for automatic speech recognition,” Proc. Interspeech 2019 , pp. 2613–2617, 2019
2019
Cited alongside, same era.
J. Kahn, A. Lee, and A. Hannun, “Self-training for end-to-end speech recognition,” in ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2020, pp. 7084–7088
2020
Closest in time.
2020
Closest in time.
2020
Closest in time.
J. He, J. Gu, J. Shen, and M. Ranzato, “Revisiting self-training for neural sequence generation,” in International Conference on Learning Representations , 2020. [Online]. Available: https://openreview.net/forum?id=SJgdnAVKDH
2020
Closest in time.
Q. Xie, M.-T. Luong, E. Hovy, and Q. V. Le, “Self-training with noisy student improves imagenet classification,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 10 687–10 698
2020
Closest in time.
A. Fan, E. Grave, and A. Joulin, “Reducing transformer depth on demand with structured dropout,” in International Conference on Learning Representations , 2020. [Online]. Available: https://openreview.net/forum?id=SylO2yStDr
2020
Closest in time.
2020
Closest in time.