Built on

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.
S. Novotney and C. Callison-Burch, “Cheap, fast and good enough: Automatic speech recognition with non-expert transcription,” in Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics , 2010, pp. 207–215
2010
Earlier work this paper cites.
G. Hinton, L. Deng, D. Yu, G. E. Dahl, A.-r. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, T. N. Sainath et al. , “Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups,” IEEE Signal processing magazine , vol. 29, no. 6, pp. 82–97, 2012
2012
Earlier work this paper cites.
A. Hannun, C. Case, J. Casper, B. Catanzaro, G. Diamos, E. Elsen, R. Prenger, S. Satheesh, S. Sengupta, A. Coates et al. , “Deep speech: Scaling up end-to-end speech recognition,” arXiv preprint arXiv:1412.5567 , 2014
Original
2014
Earlier work this paper cites.
A. Graves and N. Jaitly, “Towards end-to-end speech recognition with recurrent neural networks,” in International conference on machine learning , 2014, pp. 1764–1772
2014
Earlier work this paper cites.
D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980 , 2014
Original
2014
Earlier work this paper cites.
A. Maas, Z. Xie, D. Jurafsky, and A. Y. Ng, “Lexicon-free conversational speech recognition with neural networks,” in Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies , 2015, pp. 345–354
2015
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.
I. V. Serban, R. Lowe, P. Henderson, L. Charlin, and J. Pineau, “A survey of available corpora for building data-driven dialogue systems,” arXiv preprint arXiv:1512.05742 , 2015
Original
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 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2016, pp. 4960–4964
2016
Earlier work this paper cites.
D. Bahdanau, J. Chorowski, D. Serdyuk, P. Brakel, and Y. Bengio, “End-to-end attention-based large vocabulary speech recognition,” in 2016 IEEE international conference on acoustics, speech and signal processing (ICASSP) . IEEE, 2016, pp. 4945–4949
2016
Earlier work this paper cites.
J. Andreas, M. Rohrbach, T. Darrell, and D. Klein, “Learning to compose neural networks for question answering,” arXiv preprint arXiv:1601.01705 , 2016
Original
2016
Earlier work this paper cites.

Similar

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 770–778
2016
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G. Saon, G. Kurata, T. Sercu, K. Audhkhasi, S. Thomas, D. Dimitriadis, X. Cui, B. Ramabhadran, M. Picheny, L.-L. Lim et al. , “English conversational telephone speech recognition by humans and machines,” arXiv preprint arXiv:1703.02136 , 2017
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2017
Cited alongside, same era.
B. Liu and I. Lane, “Iterative policy learning in end-to-end trainable task-oriented neural dialog models,” in 2017 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU) . IEEE, 2017, pp. 482–489
2017
Cited alongside, same era.
A. Nagrani, J. S. Chung, and A. Zisserman, “Voxceleb: a large-scale speaker identification dataset,” arXiv preprint arXiv:1706.08612 , 2017
Original
2017
Cited alongside, same era.

Then

S. Schneider, A. Baevski, R. Collobert, and M. Auli, “wav2vec: Unsupervised pre-training for speech recognition,” arXiv preprint arXiv:1904.05862 , 2019
Original
2019
Later among the works it cites.
L. Lugosch, M. Ravanelli, P. Ignoto, V. S. Tomar, and Y. Bengio, “Speech model pre-training for end-to-end spoken language understanding,” arXiv preprint arXiv:1904.03670 , 2019
Original
2019
Later among the works it cites.
C. Zhuang, A. L. Zhai, and D. Yamins, “Local aggregation for unsupervised learning of visual embeddings,” in Proceedings of the IEEE International Conference on Computer Vision , 2019, pp. 6002–6012
2019
Later among the works it cites.
P. Bachman, R. D. Hjelm, and W. Buchwalter, “Learning representations by maximizing mutual information across views,” in Advances in Neural Information Processing Systems , 2019, pp. 15 535–15 545
2019
Later among the works it cites.

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2020

HarperValleyBank: A Domain-Specific Spoken Dialog Corpus

Wu, Mike, Nafziger, Jonathan, Scodary, Anthony et al.

Understand

We introduce HarperValleyBank, a free, public domain spoken dialog corpus.

The data simulate simple consumer banking interactions, containing about 23 hours of audio from 1,446 human-human conversations between 59 unique speakers.
We selected intents and utterance templates to allow realistic variation while controlling overall task complexity and limiting vocabulary size to about 700 unique words.
We provide audio data along with transcripts and annotations for speaker identity, caller intent, dialog actions, and emotional valence.

Built on

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.
S. Novotney and C. Callison-Burch, “Cheap, fast and good enough: Automatic speech recognition with non-expert transcription,” in Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics , 2010, pp. 207–215
2010
Earlier work this paper cites.
G. Hinton, L. Deng, D. Yu, G. E. Dahl, A.-r. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, T. N. Sainath et al. , “Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups,” IEEE Signal processing magazine , vol. 29, no. 6, pp. 82–97, 2012
2012
Earlier work this paper cites.
A. Hannun, C. Case, J. Casper, B. Catanzaro, G. Diamos, E. Elsen, R. Prenger, S. Satheesh, S. Sengupta, A. Coates et al. , “Deep speech: Scaling up end-to-end speech recognition,” arXiv preprint arXiv:1412.5567 , 2014
Original
2014
Earlier work this paper cites.
A. Graves and N. Jaitly, “Towards end-to-end speech recognition with recurrent neural networks,” in International conference on machine learning , 2014, pp. 1764–1772
2014
Earlier work this paper cites.
D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980 , 2014
Original
2014
Earlier work this paper cites.
A. Maas, Z. Xie, D. Jurafsky, and A. Y. Ng, “Lexicon-free conversational speech recognition with neural networks,” in Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies , 2015, pp. 345–354
2015
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.
I. V. Serban, R. Lowe, P. Henderson, L. Charlin, and J. Pineau, “A survey of available corpora for building data-driven dialogue systems,” arXiv preprint arXiv:1512.05742 , 2015
Original
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 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2016, pp. 4960–4964
2016
Earlier work this paper cites.
D. Bahdanau, J. Chorowski, D. Serdyuk, P. Brakel, and Y. Bengio, “End-to-end attention-based large vocabulary speech recognition,” in 2016 IEEE international conference on acoustics, speech and signal processing (ICASSP) . IEEE, 2016, pp. 4945–4949
2016
Earlier work this paper cites.
J. Andreas, M. Rohrbach, T. Darrell, and D. Klein, “Learning to compose neural networks for question answering,” arXiv preprint arXiv:1601.01705 , 2016
Original
2016
Earlier work this paper cites.

Similar

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 770–778
2016
Cited alongside, same era.
G. Saon, G. Kurata, T. Sercu, K. Audhkhasi, S. Thomas, D. Dimitriadis, X. Cui, B. Ramabhadran, M. Picheny, L.-L. Lim et al. , “English conversational telephone speech recognition by humans and machines,” arXiv preprint arXiv:1703.02136 , 2017
Original
2017
Cited alongside, same era.
B. Liu and I. Lane, “Iterative policy learning in end-to-end trainable task-oriented neural dialog models,” in 2017 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU) . IEEE, 2017, pp. 482–489
2017
Cited alongside, same era.
A. Nagrani, J. S. Chung, and A. Zisserman, “Voxceleb: a large-scale speaker identification dataset,” arXiv preprint arXiv:1706.08612 , 2017
Original
2017
Cited alongside, same era.

Then

S. Schneider, A. Baevski, R. Collobert, and M. Auli, “wav2vec: Unsupervised pre-training for speech recognition,” arXiv preprint arXiv:1904.05862 , 2019
Original
2019
Later among the works it cites.
L. Lugosch, M. Ravanelli, P. Ignoto, V. S. Tomar, and Y. Bengio, “Speech model pre-training for end-to-end spoken language understanding,” arXiv preprint arXiv:1904.03670 , 2019
Original
2019
Later among the works it cites.
C. Zhuang, A. L. Zhai, and D. Yamins, “Local aggregation for unsupervised learning of visual embeddings,” in Proceedings of the IEEE International Conference on Computer Vision , 2019, pp. 6002–6012
2019
Later among the works it cites.
P. Bachman, R. D. Hjelm, and W. Buchwalter, “Learning representations by maximizing mutual information across views,” in Advances in Neural Information Processing Systems , 2019, pp. 15 535–15 545
2019
Later among the works it cites.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

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S. Kim, T. Hori, and S. Watanabe, “Joint ctc-attention based end-to-end speech recognition using multi-task learning,” in 2017 IEEE international conference on acoustics, speech and signal processing (ICASSP) . IEEE, 2017, pp. 4835–4839

2017

Cited alongside, same era.

W. Xiong, L. Wu, F. Alleva, J. Droppo, X. Huang, and A. Stolcke, “The microsoft 2017 conversational speech recognition system,” in 2018 IEEE international conference on acoustics, speech and signal processing (ICASSP) . IEEE, 2018, pp. 5934–5938

2018

Cited alongside, same era.

C. Khatri, B. Hedayatnia, A. Venkatesh, J. Nunn, Y. Pan, Q. Liu, H. Song, A. Gottardi, S. Kwatra, S. Pancholi et al. , “Advancing the state of the art in open domain dialog systems through the alexa prize,” arXiv preprint arXiv:1812.10757 , 2018

Original

2018

Cited alongside, same era.

A. v. d. Oord, Y. Li, and O. Vinyals, “Representation learning with contrastive predictive coding,” arXiv preprint arXiv:1807.03748 , 2018

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2018

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P. Warden, “Speech commands: A dataset for limited-vocabulary speech recognition,” arXiv preprint arXiv:1804.03209 , 2018

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2018

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Z. Wu, Y. Xiong, S. Yu, and D. Lin, “Unsupervised feature learning via non-parametric instance-level discrimination,” arXiv preprint arXiv:1805.01978 , 2018

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2018

Cited alongside, same era.

T. Likhomanenko, G. Synnaeve, and R. Collobert, “Who needs words? lexicon-free speech recognition,” arXiv preprint arXiv:1904.04479 , 2019

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2019

Cited alongside, same era.

J. Chorowski, R. J. Weiss, S. Bengio, and A. van den Oord, “Unsupervised speech representation learning using wavenet autoencoders,” IEEE/ACM transactions on audio, speech, and language processing , vol. 27, no. 12, pp. 2041–2053, 2019

2019

Cited alongside, same era.

A. Baevski, H. Zhou, A. Mohamed, and M. Auli, “wav2vec 2.0: A framework for self-supervised learning of speech representations,” arXiv preprint arXiv:2006.11477 , 2020

Original

2020

Closest in time.

I. Misra and L. v. d. Maaten, “Self-supervised learning of pretext-invariant representations,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 6707–6717

2020

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K. He, H. Fan, Y. Wu, S. Xie, and R. Girshick, “Momentum contrast for unsupervised visual representation learning,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 9729–9738

2020

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T. Chen, S. Kornblith, M. Norouzi, and G. Hinton, “A simple framework for contrastive learning of visual representations,” arXiv preprint arXiv:2002.05709 , 2020

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2020

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M. Wu, C. Zhuang, M. Mosse, D. Yamins, and N. Goodman, “On mutual information in contrastive learning for visual representations,” arXiv preprint arXiv:2005.13149 , 2020

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2020

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M. Joshi, D. Chen, Y. Liu, D. S. Weld, L. Zettlemoyer, and O. Levy, “Spanbert: Improving pre-training by representing and predicting spans,” Transactions of the Association for Computational Linguistics , vol. 8, pp. 64–77, 2020

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L. Biewald, “Experiment tracking with weights and biases,” URL https://www. wandb. com/. Software available from wandb. com , 2020

2020

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