| Quartznet: Deep automatic speech recognition with 1d time-channel separable convolutions S Kriman, S Beliaev, B Ginsburg, J Huang, O Kuchaiev, V Lavrukhin, ... ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and …, 2020 | 307 | 2020 |
| Nemo: a toolkit for building ai applications using neural modules O Kuchaiev, J Li, H Nguyen, O Hrinchuk, R Leary, B Ginsburg, S Kriman, ... arXiv preprint arXiv:1909.09577, 2019 | 209 | 2019 |
| Fast conformer with linearly scalable attention for efficient speech recognition D Rekesh, NR Koluguri, S Kriman, S Majumdar, V Noroozi, H Huang, ... 2023 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU), 1-8, 2023 | 16 | 2023 |
| Nemo: a toolkit for building ai applications using neural modules (2019) O Kuchaiev, J Li, H Nguyen, O Hrinchuk, R Leary, B Ginsburg, S Kriman, ... arXiv preprint arXiv:1909.09577, 1909 | 5 | 1909 |
| Accidental learners: Spoken language identification in multilingual self-supervised models TM Bartley, F Jia, KC Puvvada, S Kriman, B Ginsburg ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and …, 2023 | 4 | 2023 |
| Investigating End-to-End ASR Architectures for Long Form Audio Transcription NR Koluguri, S Kriman, G Zelenfroind, S Majumdar, D Rekesh, V Noroozi, ... ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and …, 2024 | 1 | 2024 |
| RULER: What's the Real Context Size of Your Long-Context Language Models? CP Hsieh, S Sun, S Kriman, S Acharya, D Rekesh, F Jia, B Ginsburg arXiv preprint arXiv:2404.06654, 2024 | | 2024 |
