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References

[Yes]

Yesno. URL: http://www.openslr.org/1/.

[ABD+20]

Rosana Ardila, Megan Branson, Kelly Davis, Michael Henretty, Michael Kohler, Josh Meyer, Reuben Morais, Lindsay Saunders, Francis M. Tyers, and Gregor Weber. Common voice: a massively-multilingual speech corpus. 2020. arXiv:1912.06670.

[BZMA20]

Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, and Michael Auli. Wav2vec 2.0: a framework for self-supervised learning of speech representations. 2020. arXiv:2006.11477.

[BBL+08]

Carlos Busso, Murtaza Bulut, Chi-Chun Lee, Abe Kazemzadeh, Emily Mower Provost, Samuel Kim, Jeannette Chang, Sungbok Lee, and Shrikanth Narayanan. Iemocap: interactive emotional dyadic motion capture database. Language Resources and Evaluation, 42:335–359, 12 2008. doi:10.1007/s10579-008-9076-6.

[Cap69]

Jack Capon. High-resolution frequency-wavenumber spectrum analysis. Proceedings of the IEEE, 57(8):1408–1418, 1969.

[CPS16]

Ronan Collobert, Christian Puhrsch, and Gabriel Synnaeve. Wav2letter: an end-to-end convnet-based speech recognition system. 2016. arXiv:1609.03193.

[CBC+20]

Alexis Conneau, Alexei Baevski, Ronan Collobert, Abdelrahman Mohamed, and Michael Auli. Unsupervised cross-lingual representation learning for speech recognition. 2020. arXiv:2006.13979.

[CPC+20]

Joris Cosentino, Manuel Pariente, Samuele Cornell, Antoine Deleforge, and Emmanuel Vincent. Librimix: an open-source dataset for generalizable speech separation. 2020. arXiv:2005.11262.

[CSB+18]

Alice Coucke, Alaa Saade, Adrien Ball, Théodore Bluche, Alexandre Caulier, David Leroy, Clément Doumouro, Thibault Gisselbrecht, Francesco Caltagirone, Thibaut Lavril, and others. Snips voice platform: an embedded spoken language understanding system for private-by-design voice interfaces. arXiv preprint arXiv:1805.10190, 2018.

[Defossez21]

Alexandre Défossez. Hybrid spectrogram and waveform source separation. In Proceedings of the ISMIR 2021 Workshop on Music Source Separation. 2021.

[GKRR14]

Mark John Francis Gales, Kate Knill, Anton Ragni, and Shakti Prasad Rath. Speech recognition and keyword spotting for low-resource languages: babel project research at cued. In SLTU. 2014.

[GBP+14]

Pegah Ghahremani, Bagher BabaAli, Daniel Povey, Korbinian Riedhammer, Jan Trmal, and Sanjeev Khudanpur. A pitch extraction algorithm tuned for automatic speech recognition. In 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), volume, 2494–2498. 2014. doi:10.1109/ICASSP.2014.6854049.

[Gra12]

Alex Graves. Sequence transduction with recurrent neural networks. 2012. arXiv:1211.3711.

[GL83]

D. Griffin and Jae Lim. Signal estimation from modified short-time fourier transform. In ICASSP '83. IEEE International Conference on Acoustics, Speech, and Signal Processing, volume 8, 804–807. 1983. doi:10.1109/ICASSP.1983.1172092.

[GQC+20]

Anmol Gulati, James Qin, Chung-Cheng Chiu, Niki Parmar, Yu Zhang, Jiahui Yu, Wei Han, Shibo Wang, Zhengdong Zhang, Yonghui Wu, and Ruoming Pang. Conformer: convolution-augmented transformer for speech recognition. 2020. arXiv:2005.08100.

[HCC+14]

Awni Hannun, Carl Case, Jared Casper, Bryan Catanzaro, Greg Diamos, Erich Elsen, Ryan Prenger, Sanjeev Satheesh, Shubho Sengupta, Adam Coates, and Andrew Y. Ng. Deep speech: scaling up end-to-end speech recognition. 2014. arXiv:1412.5567.

[HIA+17]

Takuya Higuchi, Nobutaka Ito, Shoko Araki, Takuya Yoshioka, Marc Delcroix, and Tomohiro Nakatani. Online mvdr beamformer based on complex gaussian mixture model with spatial prior for noise robust asr. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 25(4):780–793, 2017.

[HIYN16]

Takuya Higuchi, Nobutaka Ito, Takuya Yoshioka, and Tomohiro Nakatani. Robust mvdr beamforming using time-frequency masks for online/offline asr in noise. In 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 5210–5214. IEEE, 2016.

[HBT+21]

Wei-Ning Hsu, Benjamin Bolte, Yao-Hung Hubert Tsai, Kushal Lakhotia, Ruslan Salakhutdinov, and Abdelrahman Mohamed. Hubert: self-supervised speech representation learning by masked prediction of hidden units. 2021. arXiv:2106.07447.

[IJ17]

Keith Ito and Linda Johnson. The lj speech dataset. https://keithito.com/LJ-Speech-Dataset/, 2017.

[KPL+22]

Jacob Kahn, Vineel Pratap, Tatiana Likhomanenko, Qiantong Xu, Awni Hannun, Jeff Cai, Paden Tomasello, Ann Lee, Edouard Grave, Gilad Avidov, and others. Flashlight: enabling innovation in tools for machine learning. arXiv preprint arXiv:2201.12465, 2022.

[KES+18a]

Nal Kalchbrenner, Erich Elsen, Karen Simonyan, Seb Noury, Norman Casagrande, Edward Lockhart, Florian Stimberg, Aaron van den Oord, Sander Dieleman, and Koray Kavukcuoglu. Efficient neural audio synthesis. 2018. arXiv:1802.08435.

[KES+18b]

Nal Kalchbrenner, Erich Elsen, Karen Simonyan, Seb Noury, Norman Casagrande, Edward Lockhart, Florian Stimberg, Aäron van den Oord, Sander Dieleman, and Koray Kavukcuoglu. Efficient neural audio synthesis. CoRR, 2018. URL: http://arxiv.org/abs/1802.08435, arXiv:1802.08435.

[KBV03]

John Kominek, Alan W Black, and Ver Ver. Cmu arctic databases for speech synthesis. Technical Report, 2003.

[LRI+19]

Loren Lugosch, Mirco Ravanelli, Patrick Ignoto, Vikrant Singh Tomar, and Yoshua Bengio. Speech model pre-training for end-to-end spoken language understanding. In Gernot Kubin and Zdravko Kacic, editors, Proc. of Interspeech, 814–818. 2019.

[LM19]

Yi Luo and Nima Mesgarani. Conv-tasnet: surpassing ideal time–frequency magnitude masking for speech separation. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 27(8):1256–1266, Aug 2019. URL: http://dx.doi.org/10.1109/TASLP.2019.2915167, doi:10.1109/taslp.2019.2915167.

[MRFB+15]

Xavier Anguera Miro, Luis Javier Rodriguez-Fuentes, Andi Buzo, Florian Metze, Igor Szoke, and Mikel Peñagarikano. Quesst2014: evaluating query-by-example speech search in a zero-resource setting with real-life queries. 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 5833–5837, 2015.

[MPG29]

RV Mises and Hilda Pollaczek-Geiringer. Praktische verfahren der gleichungsauflösung. ZAMM-Journal of Applied Mathematics and Mechanics/Zeitschrift für Angewandte Mathematik und Mechanik, 9(1):58–77, 1929.

[NCZ17]

Arsha Nagrani, Joon Son Chung, and Andrew Zisserman. Voxceleb: a large-scale speaker identification dataset. arXiv preprint arXiv:1706.08612, 2017.

[PCPK15]

Vassil Panayotov, Guoguo Chen, Daniel Povey, and Sanjeev Khudanpur. Librispeech: an asr corpus based on public domain audio books. In 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), volume, 5206–5210. 2015. doi:10.1109/ICASSP.2015.7178964.

[PCZ+19]

Daniel S. Park, William Chan, Yu Zhang, Chung-Cheng Chiu, Barret Zoph, Ekin D. Cubuk, and Quoc V. Le. Specaugment: a simple data augmentation method for automatic speech recognition. Interspeech 2019, Sep 2019. URL: http://dx.doi.org/10.21437/Interspeech.2019-2680, doi:10.21437/interspeech.2019-2680.

[PBS13]

Nathanaël Perraudin, Peter Balazs, and Peter L. Søndergaard. A fast griffin-lim algorithm. In 2013 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, volume, 1–4. 2013. doi:10.1109/WASPAA.2013.6701851.

[PXS+20]

Vineel Pratap, Qiantong Xu, Anuroop Sriram, Gabriel Synnaeve, and Ronan Collobert. Mls: a large-scale multilingual dataset for speech research. Interspeech 2020, Oct 2020. URL: http://dx.doi.org/10.21437/Interspeech.2020-2826, doi:10.21437/interspeech.2020-2826.

[RLStoter+19]

Zafar Rafii, Antoine Liutkus, Fabian-Robert Stöter, Stylianos Ioannis Mimilakis, and Rachel Bittner. MUSDB18-HQ - an uncompressed version of musdb18. December 2019. URL: https://doi.org/10.5281/zenodo.3338373, doi:10.5281/zenodo.3338373.

[RDelegliseEsteve12]

Anthony Rousseau, Paul Deléglise, and Yannick Estève. Ted-lium: an automatic speech recognition dedicated corpus. In Conference on Language Resources and Evaluation (LREC), 125–129. 2012.

[SY18]

Seyyed Saeed Sarfjoo and Junichi Yamagishi. Device recorded vctk (small subset version). 2018.

[SPW+18]

Jonathan Shen, Ruoming Pang, Ron J Weiss, Mike Schuster, Navdeep Jaitly, Zongheng Yang, Zhifeng Chen, Yu Zhang, Yuxuan Wang, Rj Skerrv-Ryan, and others. Natural tts synthesis by conditioning wavenet on mel spectrogram predictions. In 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 4779–4783. IEEE, 2018.

[SWW+21]

Yangyang Shi, Yongqiang Wang, Chunyang Wu, Ching-Feng Yeh, Julian Chan, Frank Zhang, Duc Le, and Mike Seltzer. Emformer: efficient memory transformer based acoustic model for low latency streaming speech recognition. In ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 6783–6787. 2021.

[Smi20]

Julius O. Smith. Digital audio resampling home page "theory of ideal bandlimited interpolation" section. September 2020. URL: https://ccrma.stanford.edu/~jos/resample/Theory_Ideal_Bandlimited_Interpolation.html.

[SBA09]

Mehrez Souden, Jacob Benesty, and Sofiene Affes. On optimal frequency-domain multichannel linear filtering for noise reduction. In IEEE Transactions on audio, speech, and language processing, volume 18, 260–276. IEEE, 2009.

[TEC01]

George Tzanetakis, Georg Essl, and Perry Cook. Automatic musical genre classification of audio signals. 2001. URL: http://ismir2001.ismir.net/pdf/tzanetakis.pdf.

[WRiviereL+21]

Changhan Wang, Morgane Rivière, Ann Lee, Anne Wu, Chaitanya Talnikar, Daniel Haziza, Mary Williamson, Juan Miguel Pino, and Emmanuel Dupoux. Voxpopuli: A large-scale multilingual speech corpus for representation learning, semi-supervised learning and interpretation. CoRR, 2021. URL: https://arxiv.org/abs/2101.00390, arXiv:2101.00390.

[Wei98]

R.L. Weide. The carnegie mellon pronuncing dictionary. 1998. URL: http://www.speech.cs.cmu.edu/cgi-bin/cmudict.

[YVM19]

Junichi Yamagishi, Christophe Veaux, and Kirsten MacDonald. CSTR VCTK Corpus: english multi-speaker corpus for CSTR voice cloning toolkit (version 0.92). 2019. doi:10.7488/ds/2645.

[ZDC+19]

Heiga Zen, Viet-Trung Dang, Robert A. J. Clark, Yu Zhang, Ron J. Weiss, Ye Jia, Z. Chen, and Yonghui Wu. Libritts: a corpus derived from librispeech for text-to-speech. ArXiv, 2019.

[BrianMcFeeColinRaffelDawenLiang+15]

Brian McFee, Colin Raffel, Dawen Liang, Daniel P.W. Ellis, Matt McVicar, Eric Battenberg, and Oriol Nieto. Librosa: Audio and Music Signal Analysis in Python. In Kathryn Huff and James Bergstra, editors, Proceedings of the 14th Python in Science Conference, 18 – 24. 2015. doi:10.25080/Majora-7b98e3ed-003.

[KahnRiviereZheng+20]

J. Kahn, M. Rivière, W. Zheng, E. Kharitonov, Q. Xu, P. E. Mazaré, J. Karadayi, V. Liptchinsky, R. Collobert, C. Fuegen, T. Likhomanenko, G. Synnaeve, A. Joulin, A. Mohamed, and E. Dupoux. 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), 7669–7673. 2020. https://github.com/facebookresearch/libri-light.

[Warden18]

P. Warden. Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition. ArXiv e-prints, April 2018. URL: https://arxiv.org/abs/1804.03209, arXiv:1804.03209.

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