MLS: A Large-Scale Multilingual Dataset for Speech Research

H. Zen, R. Clark, R. J. Weiss, V. Dang, Y. Jia, Y. Wu, Y. Zhang, and Z. Chen, “Libritts: A corpus derived from librispeech for text-to-speech,” in Interspeech , 2019. [Online]. Available: https://arxiv.org/abs/1904.02882

Original

1904

Earlier work this paper cites.

T. Smith and M. Waterman, “Identification of common molecular subsequences,” Journal of Molecular Biology , vol. 147, no. 1, pp. 195 – 197, 1981. [Online]. Available: http://www.sciencedirect.com/science/article/pii/0022283681900875

1981

Earlier work this paper cites.

B. E. Boser, I. M. Guyon, and V. N. Vapnik, “A training algorithm for optimal margin classifiers,” in Proceedings of the Fifth Annual Workshop on Computational Learning Theory , ser. COLT ’92. New York, NY, USA: Association for Computing Machinery, 1992, p. 144–152. [Online]. Available: https://doi.org/10.1145/130385.130401

1992

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.

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. 7, 2011

2011

Earlier work this paper cites.

Voxforge.org, “Free speech… recognition (linux, windows and mac) - voxforge.org,” http://www.voxforge.org/ , accessed 06/25/2014

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) , 2015, pp. 5206–5210

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

Cited alongside, same era.

R. Collobert, C. Puhrsch, and G. Synnaeve, “Wav2letter: an end-to-end convnet-based speech recognition system,” 2016

2016

Cited alongside, same era.

V. Liptchinsky, G. Synnaeve, and R. Collobert, “Letter-based speech recognition with gated convnets,” 2017

2017

Cited alongside, same era.

V. Manohar, D. Povey, and S. Khudanpur, “Jhu kaldi system for arabic mgb-3 asr challenge using diarization, audio-transcript alignment and transfer learning,” in 2017 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU) . IEEE, 2017, pp. 346–352

2017

Cited alongside, same era.

G. Synnaeve, Q. Xu, J. Kahn, T. Likhomanenko, E. Grave, V. Pratap, A. Sriram, V. Liptchinsky, and R. Collobert, “End-to-end asr: from supervised to semi-supervised learning with modern architectures,” 2019

2019

Later among the works it cites.

V. Pratap, A. Hannun, Q. Xu, J. Cai, J. Kahn, G. Synnaeve, V. Liptchinsky, and R. Collobert, “Wav2letter++: A fast open-source speech recognition system,” in ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , 2019, pp. 6460–6464

2019

Later among the works it cites.

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,” Interspeech 2019 , Sep 2019. [Online]. Available: http://dx.doi.org/10.21437/interspeech.2019-2680

2019

Later among the works it cites.

I. Solak, “The M-AILABS Speech Dataset,” https://www.caito.de/2019/01/the-m-ailabs-speech-dataset/ , 2019, [Online; accessed 19-April-2020]

2020