Daniel S. Park

According to our database, Daniel S. Park authored at least 16 papers between 2019 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

GridDER: Grid Detection and Evaluation in R.

Xiao Feng, Tainá Rocha, Hanna T. Thammavong, Rima Tulaiha, Xin Chen, Yingying Xie, Daniel S. Park

Ecol. Informatics, March, 2024

2023

Google USM: Scaling Automatic Speech Recognition Beyond 100 Languages.

Yu Zhang, Wei Han, James Qin, Yongqiang Wang, Ankur Bapna, Zhehuai Chen, Nanxin Chen, Bo Li, Vera Axelrod, Gary Wang, Zhong Meng, Ke Hu, Andrew Rosenberg, Rohit Prabhavalkar, Daniel S. Park, Parisa Haghani, Jason Riesa, Ginger Perng, Hagen Soltau, Trevor Strohman, Bhuvana Ramabhadran, Tara N. Sainath, Pedro J. Moreno, Chung-Cheng Chiu, Johan Schalkwyk, Françoise Beaufays, Yonghui Wu

CoRR, 2023

Noise2Music: Text-conditioned Music Generation with Diffusion Models.

Qingqing Huang, Daniel S. Park, Tao Wang, Timo I. Denk, Andy Ly, Nanxin Chen, Zhengdong Zhang, Zhishuai Zhang, Jiahui Yu, Christian Havnø Frank, Jesse H. Engel, Quoc V. Le, William Chan, Wei Han

CoRR, 2023

Discovering Adaptable Symbolic Algorithms from Scratch.

Stephen Kelly, Daniel S. Park, Xingyou Song, Mitchell McIntire, Pranav Nashikkar, Ritam Guha, Wolfgang Banzhaf, Kalyanmoy Deb, Vishnu Naresh Boddeti, Jie Tan, Esteban Real

IROS, 2023

Large-Scale Language Model Rescoring on Long-Form Data.

Tongzhou Chen, Cyril Allauzen, Yinghui Huang, Daniel S. Park, David Rybach, W. Ronny Huang, Rodrigo Cabrera, Kartik Audhkhasi, Bhuvana Ramabhadran, Pedro J. Moreno, Michael Riley

Proceedings of the IEEE International Conference on Acoustics, 2023

2022

BigSSL: Exploring the Frontier of Large-Scale Semi-Supervised Learning for Automatic Speech Recognition.

Yu Zhang, Daniel S. Park, Wei Han, James Qin, Anmol Gulati, Joel Shor, Aren Jansen, Yuanzhong Xu, Yanping Huang, Shibo Wang, Zongwei Zhou, Bo Li, Min Ma, William Chan, Jiahui Yu, Yongqiang Wang, Liangliang Cao, Khe Chai Sim, Bhuvana Ramabhadran, Tara N. Sainath, Françoise Beaufays, Zhifeng Chen, Quoc V. Le, Chung-Cheng Chiu, Ruoming Pang, Yonghui Wu

IEEE J. Sel. Top. Signal Process., 2022

G-Augment: Searching for the Meta-Structure of Data Augmentation Policies for ASR.

Gary Wang, Ekin D. Cubuk, Andrew Rosenberg, Shuyang Cheng, Ron J. Weiss, Bhuvana Ramabhadran, Pedro J. Moreno, Quoc V. Le, Daniel S. Park

Proceedings of the IEEE Spoken Language Technology Workshop, 2022

Universal Paralinguistic Speech Representations Using self-Supervised Conformers.

Joel Shor, Aren Jansen, Wei Han, Daniel S. Park, Yu Zhang

Proceedings of the IEEE International Conference on Acoustics, 2022

2021

SpeechStew: Simply Mix All Available Speech Recognition Data to Train One Large Neural Network.

William Chan, Daniel S. Park, Chris A. Lee, Yu Zhang, Quoc V. Le, Mohammad Norouzi

CoRR, 2021

Efficient Knowledge Distillation for RNN-Transducer Models.

Sankaran Panchapagesan, Daniel S. Park, Chung-Cheng Chiu, Yuan Shangguan, Qiao Liang, Alexander Gruenstein

Proceedings of the IEEE International Conference on Acoustics, 2021

2020

Towards NNGP-guided Neural Architecture Search.

Daniel S. Park, Jaehoon Lee, Daiyi Peng, Yuan Cao, Jascha Sohl-Dickstein

CoRR, 2020

Pushing the Limits of Semi-Supervised Learning for Automatic Speech Recognition.

Yu Zhang, James Qin, Daniel S. Park, Wei Han, Chung-Cheng Chiu, Ruoming Pang, Quoc V. Le, Yonghui Wu

CoRR, 2020

Improved Noisy Student Training for Automatic Speech Recognition.

Daniel S. Park, Yu Zhang, Ye Jia, Wei Han, Chung-Cheng Chiu, Bo Li, Yonghui Wu, Quoc V. Le

Proceedings of the 21st Annual Conference of the International Speech Communication Association, 2020

Specaugment on Large Scale Datasets.

Daniel S. Park, Yu Zhang, Chung-Cheng Chiu, Youzheng Chen, Bo Li, William Chan, Quoc V. Le, Yonghui Wu

Proceedings of the 2020 IEEE International Conference on Acoustics, 2020

2019

SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition.

Daniel S. Park, William Chan, Yu Zhang, Chung-Cheng Chiu, Barret Zoph, Ekin D. Cubuk, Quoc V. Le

Proceedings of the 20th Annual Conference of the International Speech Communication Association, 2019

The Effect of Network Width on Stochastic Gradient Descent and Generalization: an Empirical Study.

Daniel S. Park, Jascha Sohl-Dickstein, Quoc V. Le, Samuel L. Smith

Proceedings of the 36th International Conference on Machine Learning, 2019