| Data2vec: A general framework for self-supervised learning in speech, vision and language A Baevski, WN Hsu, Q Xu, A Babu, J Gu, M Auli International Conference on Machine Learning, 2022, 2022 | 871 | 2022 |
| Libri-Light: A Benchmark for ASR with Limited or No Supervision J Kahn*, M Rivière*, W Zheng*, E Kharitonov*, Q Xu*, PE Mazaré*, ... ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and …, 2019 | 678 | 2019 |
| Xls-r: Self-supervised cross-lingual speech representation learning at scale A Babu, C Wang, A Tjandra, K Lakhotia, Q Xu, N Goyal, K Singh, ... INTERSPEECH 2022, 2021 | 649 | 2021 |
| Mls: A large-scale multilingual dataset for speech research V Pratap, Q Xu, A Sriram, G Synnaeve, R Collobert INTERSPEECH 2020, 2020 | 493 | 2020 |
| An empirical study on evaluation metrics of generative adversarial networks Q Xu, G Huang, Y Yuan, C Guo, Y Sun, F Wu, K Weinberger arXiv preprint arXiv:1806.07755, 2018 | 390 | 2018 |
| End-to-end ASR: from Supervised to Semi-Supervised Learning with Modern Architectures G Synnaeve*, Q Xu*, J Kahn*, E Grave*, T Likhomanenko, V Pratap, ... ICML 2020 Workshop on Self-supervision in Audio and Speech, 2019 | 276 | 2019 |
| Robust wav2vec 2.0: Analyzing Domain Shift in Self-Supervised Pre-Training WN Hsu, A Sriram, A Baevski, T Likhomanenko, Q Xu, V Pratap, J Kahn, ... INTERSPEECH 2021, 2021 | 251 | 2021 |
| Wav2letter++: A fast open-source speech recognition system V Pratap, A Hannun, Q Xu, J Cai, J Kahn, G Synnaeve, V Liptchinsky, ... ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and …, 2019 | 239 | 2019 |
| Self-training and Pre-training are Complementary for Speech Recognition Q Xu*, A Baevski*, T Likhomanenko, P Tomasello, A Conneau, ... ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and …, 2020 | 188 | 2020 |
| Iterative pseudo-labeling for speech recognition Q Xu, T Likhomanenko, J Kahn, A Hannun, G Synnaeve, R Collobert INTERSPEECH 2020, 2020 | 150 | 2020 |
| Sequence-to-Sequence Speech Recognition with Time-Depth Separable Convolutions A Hannun, A Lee, Q Xu, R Collobert INTERSPEECH 2019, 2019 | 115 | 2019 |
| Fully convolutional speech recognition N Zeghidour*, Q Xu*, V Liptchinsky, N Usunier, G Synnaeve, R Collobert arXiv preprint arXiv:1812.06864, 2018 | 113 | 2018 |
| Rethinking Evaluation in ASR: Are Our Models Robust Enough? T Likhomanenko*, Q Xu*, V Pratap, P Tomasello, J Kahn, G Avidov, ... INTERSPEECH 2021, 2020 | 105 | 2020 |
| Simple and effective zero-shot cross-lingual phoneme recognition Q Xu, A Baevski, M Auli INTERSPEECH 2022, 2021 | 81 | 2021 |
| On the tool manipulation capability of open-source large language models Q Xu, F Hong, B Li, C Hu, Z Chen, J Zhang arXiv preprint arXiv:2305.16504, 2023 | 77 | 2023 |
| slimipl: Language-model-free iterative pseudo-labeling T Likhomanenko, Q Xu, J Kahn, G Synnaeve, R Collobert INTERSPEECH 2021, 2020 | 63 | 2020 |
| Self-training for end-to-end speech translation J Pino, Q Xu, X Ma, MJ Dousti, Y Tang INTERSPEECH 2020, 2020 | 63 | 2020 |
| CAPE: Encoding Relative Positions with Continuous Augmented Positional Embeddings T Likhomanenko, Q Xu, R Collobert, G Synnaeve, A Rogozhnikov Thirty-fifth Conference on Neural Information Processing Systems (NeurIPS), 2021 | 51 | 2021 |
| Scaling Up Online Speech Recognition Using ConvNets V Pratap, Q Xu, J Kahn, G Avidov, T Likhomanenko, A Hannun, ... INTERSPEECH 2020, 2020 | 49 | 2020 |
| Flashlight: Enabling innovation in tools for machine learning JD Kahn, V Pratap, T Likhomanenko, Q Xu, A Hannun, J Cai, P Tomasello, ... International Conference on Machine Learning, 10557-10574, 2022 | 25 | 2022 |
Ronan CollobertMachine Learning Research, AppleE-mail confirmado em apple.com