@article{wavernn,
  author    = {Nal Kalchbrenner and
               Erich Elsen and
               Karen Simonyan and
               Seb Noury and
               Norman Casagrande and
               Edward Lockhart and
               Florian Stimberg and
               A{\"{a}}ron van den Oord and
               Sander Dieleman and
               Koray Kavukcuoglu},
  title     = {Efficient Neural Audio Synthesis},
  journal   = {CoRR},
  volume    = {abs/1802.08435},
  year      = {2018},
  url       = {http://arxiv.org/abs/1802.08435},
  eprinttype = {arXiv},
  eprint    = {1802.08435},
  timestamp = {Mon, 13 Aug 2018 16:47:01 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-1802-08435.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}
@misc{RESAMPLE,
	author = {Julius O. Smith},
	title = {Digital Audio Resampling Home Page "Theory of Ideal Bandlimited Interpolation" section},
	url = {https://ccrma.stanford.edu/~jos/resample/Theory_Ideal_Bandlimited_Interpolation.html},
	month = {September},
	year = {2020}
}
@article{voxpopuli,
  author    = {Changhan Wang and
               Morgane Rivi{\`{e}}re and
               Ann Lee and
               Anne Wu and
               Chaitanya Talnikar and
               Daniel Haziza and
               Mary Williamson and
               Juan Miguel Pino and
               Emmanuel Dupoux},
  title     = {VoxPopuli: {A} Large-Scale Multilingual Speech Corpus for Representation
               Learning, Semi-Supervised Learning and Interpretation},
  journal   = {CoRR},
  volume    = {abs/2101.00390},
  year      = {2021},
  url       = {https://arxiv.org/abs/2101.00390},
  eprinttype = {arXiv},
  eprint    = {2101.00390},
  timestamp = {Thu, 12 Aug 2021 15:37:06 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2101-00390.bib},
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}
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   year={2019},
   month={Sep}
}
@misc{ljspeech17,
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  year         = {2017}
}
@misc{conneau2020unsupervised,
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      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
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}
@misc{ardila2020common,
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      year={2020},
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}
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}
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  booktitle={ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, 
  title={Libri-Light: A Benchmark for ASR with Limited or No Supervision}, 
  year={2020},
  pages={7669-7673},
  note = {\url{https://github.com/facebookresearch/libri-light}},
}
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}
@misc{baevski2020wav2vec,
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}
@misc{hsu2021hubert,
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      year={2021},
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      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
@misc{hannun2014deep,
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      year={2014},
      eprint={1412.5567},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
@misc{graves2012sequence,
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      author={Alex Graves},
      year={2012},
      eprint={1211.3711},
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}
@misc{collobert2016wav2letter,
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      author={Ronan Collobert and Christian Puhrsch and Gabriel Synnaeve},
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      eprint={1609.03193},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}
@misc{kalchbrenner2018efficient,
      title={Efficient Neural Audio Synthesis}, 
      author={Nal Kalchbrenner and Erich Elsen and Karen Simonyan and Seb Noury and Norman Casagrande and Edward Lockhart and Florian Stimberg and Aaron van den Oord and Sander Dieleman and Koray Kavukcuoglu},
      year={2018},
      eprint={1802.08435},
      archivePrefix={arXiv},
      primaryClass={cs.SD}
}
@misc{gulati2020conformer,
      title={Conformer: Convolution-augmented Transformer for Speech Recognition},
      author={Anmol Gulati and James Qin and Chung-Cheng Chiu and Niki Parmar and Yu Zhang and Jiahui Yu and Wei Han and Shibo Wang and Zhengdong Zhang and Yonghui Wu and Ruoming Pang},
      year={2020},
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      archivePrefix={arXiv},
      primaryClass={eess.AS}
}
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   DOI={10.1109/taslp.2019.2915167},
   number={8},
   journal={IEEE/ACM Transactions on Audio, Speech, and Language Processing},
   publisher={Institute of Electrical and Electronics Engineers (IEEE)},
   author={Luo, Yi and Mesgarani, Nima},
   year={2019},
   month={Aug},
   pages={1256–1266}
}
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}
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@inproceedings{shen2018natural,
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@inproceedings{shi2021emformer,
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@article{kahn2022flashlight,
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}
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@misc{cosentino2020librimix,
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  year={2020},
  eprint={2005.11262},
  archivePrefix={arXiv},
  primaryClass={eess.AS}
}
@article{Zen2019LibriTTSAC,
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}
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  month = apr,
  url = {https://arxiv.org/abs/1804.03209},
}
@inproceedings{rousseau2012tedlium,
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@misc{yamagishi2019vctk,
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}
@misc{Sarfjoo2018DeviceRV,
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}
@misc{tzanetakis_essl_cook_2001,
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}
@article{Mir2015QUESST2014EQ,
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}
@misc{cmudict,
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@misc{YesNo,
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}
@misc{MUSDB18HQ,
  author       = {Rafii, Zafar and Liutkus, Antoine and Fabian-Robert St{\"o}ter and Mimilakis, Stylianos Ioannis and
                  Bittner, Rachel},
  title        = {{MUSDB18-HQ} - an uncompressed version of MUSDB18},
  month        = dec,
  year         = 2019,
  doi          = {10.5281/zenodo.3338373},
  url          = {https://doi.org/10.5281/zenodo.3338373}
}
@inproceedings{fluent,
  author    = {Loren Lugosch and Mirco Ravanelli and Patrick Ignoto and Vikrant Singh Tomar and Yoshua Bengio},
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  year      = {2019},
}
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}
@inproceedings{defossez2021hybrid,
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@article{CATTONI2021101155,
title = {MuST-C: A multilingual corpus for end-to-end speech translation},
journal = {Computer Speech & Language},
volume = {66},
pages = {101155},
year = {2021},
issn = {0885-2308},
doi = {https://doi.org/10.1016/j.csl.2020.101155},
url = {https://www.sciencedirect.com/science/article/pii/S0885230820300887},
author = {Roldano Cattoni and Mattia Antonino {Di Gangi} and Luisa Bentivogli and Matteo Negri and Marco Turchi},
keywords = {Spoken language translation, Multilingual corpus},
abstract = {End-to-end spoken language translation (SLT) has recently gained popularity thanks to the advancement of sequence to sequence learning in its two parent tasks: automatic speech recognition (ASR) and machine translation (MT). However, research in the field has to confront with the scarcity of publicly available corpora to train data-hungry neural networks. Indeed, while traditional cascade solutions can build on sizable ASR and MT training data for a variety of languages, the available SLT corpora suitable for end-to-end training are few, typically small and of limited language coverage. We contribute to fill this gap by presenting MuST-C, a large and freely available Multilingual Speech Translation Corpus built from English TED Talks. Its unique features include: i) language coverage and diversity (from English into 14 languages from different families), ii) size (at least 237 hours of transcribed recordings per language, 430 on average), iii) variety of topics and speakers, and iv) data quality. Besides describing the corpus creation methodology and discussing the outcomes of empirical and manual quality evaluations, we present baseline results computed with strong systems on each language direction covered by MuST-C.}
}
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}
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  year={2022},
  volume={},
  number={},
  pages={8862-8866},
  doi={10.1109/ICASSP43922.2022.9746490}}
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}
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 pages = {17022--17033},
 publisher = {Curran Associates, Inc.},
 title = {HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis},
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 year = {2020}
}
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}
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}
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  booktitle={2021 IEEE Spoken Language Technology Workshop (SLT)},
  pages={652--658},
  year={2021},
  organization={IEEE}
}
