| FPGA-based low-power speech recognition with recurrent neural networks M Lee, K Hwang, J Park, S Choi, S Shin, W Sung 2016 IEEE International Workshop on Signal Processing Systems (SiPS), 230-235, 2016 | 98 | 2016 |
| FPGA based implementation of deep neural networks using on-chip memory only J Park, W Sung 2016 IEEE International conference on acoustics, speech and signal …, 2016 | 89 | 2016 |
| Fully neural network based speech recognition on mobile and embedded devices J Park, Y Boo, I Choi, S Shin, W Sung Advances in neural information processing systems 31, 2018 | 49 | 2018 |
| Conformer-based on-device streaming speech recognition with KD compression and two-pass architecture J Park, S Jin, J Park, S Kim, D Sandhyana, C Lee, M Han, J Lee, S Jung, ... 2022 IEEE Spoken Language Technology Workshop (SLT), 92-99, 2023 | 8 | 2023 |
| Simple gated convnet for small footprint acoustic modeling L Lee, J Park, W Sung 2019 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU …, 2019 | 7 | 2019 |
| Hlhlp: Quantized neural networks training for reaching flat minima in loss surface S Shin, J Park, Y Boo, W Sung Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 5784-5791, 2020 | 6 | 2020 |
| Convolution-based attention model with positional encoding for streaming speech recognition on embedded devices J Park, C Kim, W Sung 2021 IEEE Spoken Language Technology Workshop (SLT), 30-37, 2021 | 5 | 2021 |
| Single stream parallelization of recurrent neural networks for low power and fast inference W Sung, J Park arXiv preprint arXiv:1803.11389, 2018 | 5 | 2018 |
| S-SGD: Symmetrical stochastic gradient descent with weight noise injection for reaching flat minima W Sung, I Choi, J Park, S Choi, S Shin arXiv preprint arXiv:2009.02479, 2020 | 4 | 2020 |
| Character-level Language Modeling with Gated Hierarchical Recurrent Neural Networks. I Choi, J Park, W Sung INTERSPEECH, 411-415, 2018 | 4 | 2018 |
| Low-latency lightweight streaming speech recognition with 8-bit quantized simple gated convolutional neural networks J Park, X Qian, Y Jo, W Sung ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and …, 2020 | 3 | 2020 |
| Effect of Adding Positional Information on Convolutional Neural Networks for End-to-End Speech Recognition. J Park, W Sung INTERSPEECH, 46-50, 2020 | 1 | 2020 |
| Exploration of on-device end-to-end acoustic modeling with neural networks W Sung, L Lee, J Park 2019 IEEE International Workshop on Signal Processing Systems (SiPS), 160-165, 2019 | 1 | 2019 |
| Hierarchical Recurrent Neural Networks for Acoustic Modeling. J Park, I Choi, Y Boo, W Sung INTERSPEECH, 3728-3732, 2018 | 1 | 2018 |
| Architecture exploration of a programmable neural network processor for embedded systems W Sung, J Park 2016 International Conference on Embedded Computer Systems: Architectures …, 2016 | 1 | 2016 |
| On the compression of shallow non-causal ASR models using knowledge distillation and tied-and-reduced decoder for low-latency on-device speech recognition N Adiga, J Park, CS Kumar, S Singh, K Lee, C Kim, D Gowda arXiv preprint arXiv:2312.09842, 2023 | | 2023 |
| A More Accurate Internal Language Model Score Estimation for the Hybrid Autoregressive Transducer K Lee, H Kim, S Jin, J Park, Y Han INTERSPEECH, 869-873, 2023 | | 2023 |
| Electronic apparatus for speech recognition, and controlling method thereof P Jinhwan, S Kim, JIN Sichen, J Park, D Sandhyana, HAN Changwoo US Patent App. 17/968,517, 2023 | | 2023 |
| Macro-block dropout for improved regularization in training end-to-end speech recognition models C Kim, S Indurti, J Park, W Sung 2022 IEEE Spoken Language Technology Workshop (SLT), 331-338, 2023 | | 2023 |
| On-Device End-to-end Speech Recognition with Multi-Step Parallel Rnns Y Boo, J Park, L Lee, W Sung 2018 IEEE Spoken Language Technology Workshop (SLT), 376-381, 2018 | | 2018 |
Wonyong SungProfessor, IEEE Life Fellow, Graduate School of Engineering Practice, Seoul National UniversityVerified email at snu.ac.kr
