Daniel S. Park

Orcid: 0000-0002-1919-0460

According to our database1, Daniel S. Park authored at least 17 papers between 2019 and 2024.

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Bibliography

2024
GridDER: Grid Detection and Evaluation in R.
Ecol. Informatics, March, 2024

2023
Google USM: Scaling Automatic Speech Recognition Beyond 100 Languages.
CoRR, 2023

Noise2Music: Text-conditioned Music Generation with Diffusion Models.
CoRR, 2023


Large-Scale Language Model Rescoring on Long-Form Data.
Proceedings of the IEEE International Conference on Acoustics, 2023

2022
BigSSL: Exploring the Frontier of Large-Scale Semi-Supervised Learning for Automatic Speech Recognition.
IEEE J. Sel. Top. Signal Process., 2022

G-Augment: Searching for the Meta-Structure of Data Augmentation Policies for ASR.
Proceedings of the IEEE Spoken Language Technology Workshop, 2022

Universal Paralinguistic Speech Representations Using self-Supervised Conformers.
Proceedings of the IEEE International Conference on Acoustics, 2022

2021
Universal Paralinguistic Speech Representations Using Self-Supervised Conformers.
CoRR, 2021

SpeechStew: Simply Mix All Available Speech Recognition Data to Train One Large Neural Network.
CoRR, 2021

Efficient Knowledge Distillation for RNN-Transducer Models.
Proceedings of the IEEE International Conference on Acoustics, 2021

2020
Towards NNGP-guided Neural Architecture Search.
CoRR, 2020

Pushing the Limits of Semi-Supervised Learning for Automatic Speech Recognition.
CoRR, 2020

Improved Noisy Student Training for Automatic Speech Recognition.
Proceedings of the Interspeech 2020, 2020

Specaugment on Large Scale Datasets.
Proceedings of the 2020 IEEE International Conference on Acoustics, 2020

2019
SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition.
Proceedings of the Interspeech 2019, 2019

The Effect of Network Width on Stochastic Gradient Descent and Generalization: an Empirical Study.
Proceedings of the 36th International Conference on Machine Learning, 2019


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