Fetching the paper…
Reading the bibliography…
Error correction models form an important part of Automatic Speech Recognition (ASR) post-processing to improve the readability and quality of transcriptions.
M. Auli, M. Galley, C. Quirk, and G. Zweig, “Joint language and translation modeling with recurrent neural networks,” in Proc. EMNLP , 2013
2013
Earlier work this paper cites.
A. Graves and N. Jaitly, “Towards end-to-end speech recognition with recurrent neural networks,” in Proc. International Conference on Machine Learning . PMLR, 2014, pp. 1764–1772
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 Proc. 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2015, pp. 5206–5210
2015
Earlier work this paper cites.
W. Chan, N. Jaitly, Q. Le, and O. Vinyals, “Listen, attend and spell: A neural network for large vocabulary conversational speech recognition,” in Proc. 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2016, pp. 4960–4964
2016
Earlier work this paper cites.
X. Liu, X. Chen, Y. Wang, M. J. Gales, and P. C. Woodland, “Two efficient lattice rescoring methods using recurrent neural network language models,” IEEE/ACM Transactions on Audio, Speech, and Language Processing , vol. 24, no. 8, pp. 1438–1449, 2016
2016
Earlier work this paper cites.
M. B. Hoy, “Alexa, Siri, Cortana, and more: an introduction to voice assistants,” Medical Reference Services Quarterly , vol. 37, no. 1, pp. 81–88, 2018
2018
Earlier work this paper cites.
R. Errattahi, A. El Hannani, and H. Ouahmane, “Automatic speech recognition errors detection and correction: A review,” Procedia Computer Science , vol. 128, pp. 32–37, 2018
2018
Earlier work this paper cites.
H. Xu, T. Chen, D. Gao, Y. Wang, K. Li, N. Goel, Y. Carmiel, D. Povey, and S. Khudanpur, “A pruned RNNLM lattice-rescoring algorithm for automatic speech recognition,” in Proc. 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2018, pp. 5929–5933
2018
Earlier work this paper cites.
S. Watanabe, T. Hori, S. Karita, T. Hayashi, J. Nishitoba, Y. Unno, N.-E. Y. Soplin, J. Heymann, M. Wiesner, N. Chen et al. , “ESPnet: End-to-End Speech Processing Toolkit,” in Proc. Interspeech 2018 , 2018, pp. 2207–2211
2018
Earlier work this paper cites.
2019
Earlier work this paper cites.
P. Zhang, B. Chen, N. Ge, and K. Fan, “Lattice transformer for speech translation,” in Proc. 57th Annual Meeting of the Association for Computational Linguistics , 2019, pp. 6475–6484
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,” in Proc. Interspeech 2019 , 2019, pp. 2613–2617
2019
Cited alongside, same era.
J. Guo, T. N. Sainath, and R. J. Weiss, “A spelling correction model for end-to-end speech recognition,” in Proc. 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2019, pp. 5651–5655
2019
Cited alongside, same era.
2019
Cited alongside, same era.
S. Dathathri, A. Madotto, J. Lan, J. Hung, E. Frank, P. Molino, J. Yosinski, and R. Liu, “Plug and play language models: A simple approach to controlled text generation,” in Proc. International Conference on Learning Representations , 2020
2020
Later among the works it cites.
Y. Zhao, X. Yang, J. Wang, Y. Gao, C. Yan, and Y. Zhou, “BART based semantic correction for Mandarin automatic speech recognition system,” in Proc. Interspeech 2021 , 2021
2021
Later among the works it cites.
L. Zhu, W. Liu, L. Liu, and E. Lin, “Improving ASR error correction using n-best hypotheses,” in Proc. 2021 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU) . IEEE, 2021, pp. 83–89
2021
Later among the works it cites.
Y. Leng, X. Tan, R. Wang, L. Zhu, J. Xu, W. Liu, L. Liu, X.-Y. Li, T. Qin, E. Lin et al. , “Fastcorrect 2: Fast error correction on multiple candidates for automatic speech recognition,” in Proc. Findings of the Association for Computational Linguistics: EMNLP 2021 , 2021, pp. 4328–4337
2021
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
A. Gulati, J. Qin, C.-C. Chiu, N. Parmar, Y. Zhang, J. Yu, W. Han, S. Wang, Z. Zhang, Y. Wu et al. , “Conformer: Convolution-augmented transformer for speech recognition,” in Proc. Interspeech 2020 , 2020, pp. 5036–5040
2020
Cited alongside, same era.
O. Hrinchuk, M. Popova, and B. Ginsburg, “Correction of automatic speech recognition with transformer sequence-to-sequence model,” in Proc. 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2020, pp. 7074–7078
2020
Cited alongside, same era.
Y. Weng, S. S. Miryala, C. Khatri, R. Wang, H. Zheng, P. Molino, M. Namazifar, A. Papangelis, H. Williams, F. Bell et al. , “Joint contextual modeling for ASR correction and language understanding,” in Proc. 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2020, pp. 6349–6353
2020
Cited alongside, same era.
R. Ma, H. Li, Q. Liu, L. Chen, and K. Yu, “Neural lattice search for speech recognition,” in Proc. 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2020, pp. 7794–7798
2020
Cited alongside, same era.
H. Wang, S. Dong, Y. Liu, J. Logan, A. K. Agrawal, and Y. Liu, “ASR error correction with augmented transformer for entity retrieval,” in Proc. Interspeech 2020 , 2020
2020
Cited alongside, same era.
C. Raffel, N. Shazeer, A. Roberts, K. Lee, S. Narang, M. Matena, Y. Zhou, W. Li, and P. J. Liu, “Exploring the limits of transfer learning with a unified text-to-text transformer,” Journal of Machine Learning Research , vol. 21, no. 1, pp. 5485–5551, 2020
2020
Cited alongside, same era.
Later among the works it cites.
R. Prabhavalkar, Y. He, D. Rybach, S. Campbell, A. Narayanan, T. Strohman, and T. N. Sainath, “Less is more: Improved RNN-T decoding using limited label context and path merging,” in Proc. 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2021, pp. 5659–5663
2021
Later among the works it cites.
2022
Later among the works it cites.
2022
Later among the works it cites.
L. Dai, L. Chen, Z. Zhou, and K. Yu, “LatticeBART: Lattice-to-lattice pre-training for speech recognition,” in Proc. 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2022, pp. 6112–6116
2022
Later among the works it cites.
M. Novak, P. Papadopoulos, and A. A. AI, “RNN-T lattice enhancement by grafting of pruned paths,” in Proc. Interspeech 2022 , 2022, pp. 4960–4964
2022
Later among the works it cites.