Daniel S. Park

Publication Activity (10 Years)

Years Active: 2019-2024
Publications (10 Years): 26

Top Topics

Manifold Regularization

Automatic Speech Recognition

Neural Architecture

Supervised Learning

Top Venues

IEEE J. Sel. Top. Signal Process.

Publications

Xiao Feng, Tainá Rocha, Hanna T. Thammavong, Rima Tulaiha, Xin Chen, Yingying Xie, Daniel S. Park
GridDER: Grid Detection and Evaluation in R. Ecol. Informatics 79 (2024)
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
Large-Scale Language Model Rescoring on Long-Form Data. ICASSP (2023)
Stephen Kelly, Daniel S. Park, Xingyou Song, Mitchell McIntire, Pranav Nashikkar, Ritam Guha, Wolfgang Banzhaf, Kalyanmoy Deb, Vishnu Naresh Boddeti, Jie Tan, Esteban Real
Discovering Adaptable Symbolic Algorithms from Scratch. IROS (2023)
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
Noise2Music: Text-conditioned Music Generation with Diffusion Models. CoRR (2023)
Stephen Kelly, Daniel S. Park, Xingyou Song, Mitchell McIntire, Pranav Nashikkar, Ritam Guha, Wolfgang Banzhaf, Kalyanmoy Deb, Vishnu Naresh Boddeti, Jie Tan, Esteban Real
Discovering Adaptable Symbolic Algorithms from Scratch. CoRR (2023)
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
Large-scale Language Model Rescoring on Long-form Data. CoRR (2023)
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
Google USM: Scaling Automatic Speech Recognition Beyond 100 Languages. CoRR (2023)
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
BigSSL: Exploring the Frontier of Large-Scale Semi-Supervised Learning for Automatic Speech Recognition. IEEE J. Sel. Top. Signal Process. 16 (6) (2022)
Gary Wang, Ekin D. Cubuk, Andrew Rosenberg, Shuyang Cheng, Ron J. Weiss, Bhuvana Ramabhadran, Pedro J. Moreno, Quoc V. Le, Daniel S. Park
G-Augment: Searching for the Meta-Structure of Data Augmentation Policies for ASR. SLT (2022)
Joel Shor, Aren Jansen, Wei Han, Daniel S. Park, Yu Zhang
Universal Paralinguistic Speech Representations Using self-Supervised Conformers. ICASSP (2022)
Gary Wang, Ekin D. Cubuk, Andrew Rosenberg, Shuyang Cheng, Ron J. Weiss, Bhuvana Ramabhadran, Pedro J. Moreno, Quoc V. Le, Daniel S. Park
G-Augment: Searching for the Meta-Structure of Data Augmentation Policies for ASR. CoRR (2022)
Sankaran Panchapagesan, Daniel S. Park, Chung-Cheng Chiu, Yuan Shangguan, Qiao Liang, Alexander Gruenstein
Efficient Knowledge Distillation for RNN-Transducer Models. ICASSP (2021)
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
BigSSL: Exploring the Frontier of Large-Scale Semi-Supervised Learning for Automatic Speech Recognition. CoRR (2021)
William Chan, Daniel S. Park, Chris A. Lee, Yu Zhang, Quoc V. Le, Mohammad Norouzi
SpeechStew: Simply Mix All Available Speech Recognition Data to Train One Large Neural Network. CoRR (2021)
Joel Shor, Aren Jansen, Wei Han, Daniel S. Park, Yu Zhang
Universal Paralinguistic Speech Representations Using Self-Supervised Conformers. CoRR (2021)
Yu Zhang, James Qin, Daniel S. Park, Wei Han, Chung-Cheng Chiu, Ruoming Pang, Quoc V. Le, Yonghui Wu
Pushing the Limits of Semi-Supervised Learning for Automatic Speech Recognition. CoRR (2020)
Daniel S. Park, Yu Zhang, Chung-Cheng Chiu, Youzheng Chen, Bo Li, William Chan, Quoc V. Le, Yonghui Wu
Specaugment on Large Scale Datasets. ICASSP (2020)
Daniel S. Park, Yu Zhang, Ye Jia, Wei Han, Chung-Cheng Chiu, Bo Li, Yonghui Wu, Quoc V. Le
Improved Noisy Student Training for Automatic Speech Recognition. CoRR (2020)
Sankaran Panchapagesan, Daniel S. Park, Chung-Cheng Chiu, Yuan Shangguan, Qiao Liang, Alexander Gruenstein
Efficient Knowledge Distillation for RNN-Transducer Models. CoRR (2020)
Daniel S. Park, Jaehoon Lee, Daiyi Peng, Yuan Cao, Jascha Sohl-Dickstein
Towards NNGP-guided Neural Architecture Search. CoRR (2020)
Daniel S. Park, Yu Zhang, Ye Jia, Wei Han, Chung-Cheng Chiu, Bo Li, Yonghui Wu, Quoc V. Le
Improved Noisy Student Training for Automatic Speech Recognition. INTERSPEECH (2020)
Daniel S. Park, William Chan, Yu Zhang, Chung-Cheng Chiu, Barret Zoph, Ekin D. Cubuk, Quoc V. Le
SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition. CoRR (2019)
Daniel S. Park, William Chan, Yu Zhang, Chung-Cheng Chiu, Barret Zoph, Ekin D. Cubuk, Quoc V. Le
SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition. INTERSPEECH (2019)
Daniel S. Park, Jascha Sohl-Dickstein, Quoc V. Le, Samuel L. Smith
The Effect of Network Width on Stochastic Gradient Descent and Generalization: an Empirical Study. CoRR (2019)
Daniel S. Park, Yu Zhang, Chung-Cheng Chiu, Youzheng Chen, Bo Li, William Chan, Quoc V. Le, Yonghui Wu
SpecAugment on Large Scale Datasets. CoRR (2019)
Daniel S. Park, Jascha Sohl-Dickstein, Quoc V. Le, Samuel L. Smith
The Effect of Network Width on Stochastic Gradient Descent and Generalization: an Empirical Study. ICML (2019)