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Daniel Park

Daniel Park has 2 papers in our library, in 2019–2023, most of them on Language models & pretraining, Speech & spoken language; often with Barret Zoph, Bhuvana Ramabhadran, Chung-Cheng Chiu.

2 papers in our library · 2019–2023

OpenAlex · ORCID · h-index 14 · 35 works · 4,778 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 489 times in our library · 8 more in our library without a page yet · ⭐ Paper Stars ranking: #836

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

🎓Azusa Pacific UniversityAzusa · United States2005–2025 · 2 paperscurrent🏥Vanderbilt University Medical CenterNashville · United States2023–2025 · 2 paperscurrent🎓Louisiana State UniversityBaton Rouge · United States2025 · 1 papercurrent🏢GoogleMountain View · United States2019–2023 · 12 papers🎓Rensselaer Polytechnic InstituteTroy · United States2019–2022 · 4 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 1 more institution on single bylines are not listed.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksLanguage and Linguistics · 2 worksSignal Processing · 2 works

🧭 Topics 2Research subjects and languages the work is about.Speech & spoken language · 1 workLanguage models & pretraining · 1 work

 Works by year

2020s

20231 paper

2010s

20191 paper

 Papers

The most cited first (OpenAlex and our library).

  1. SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition (2019 · 3,837 citations)
    End-to-end speech recognizers overfit easily and need large amounts of training data. Earlier augmentation for ASR mostly changed the raw audio, for example by adding noise, perturbing speed or simulating rooms. The authors propose SpecAugment, which instead alters the log mel spectrogram, treating it like an image…
  2. Large-scale Language Model Rescoring on Long-form Data (2023 · 21 citations)
    The paper asks whether very large language models help speech recognition of long recordings, using 30-minute YouTube videos as the test material.

All works

10 works of theirs in our library, the most cited first; 2 with a page here, the others link their original until we summarise them.

  1. SpecAugment: A Simple Data Augmentation Method for Automatic Speech RecognitionInterspeechDaniel S. Park, William Chan, Yu Zhang, et al.2019 · 3,837 citations
  2. Improved Noisy Student Training for Automatic Speech Recognition ↗Interspeech (USB) · no summary yetDaniel Park et al.2020 · 251 citations
  3. Pushing the Limits of Semi-Supervised Learning for Automatic Speech Recognition ↗arXiv (Cornell University) · no summary yetYu Zhang et al.2020 · 233 citations
  4. BigSSL: Exploring the Frontier of Large-Scale Semi-Supervised Learning for Automatic Speech Recognition ↗IEEE Journal of Selected Topics in Signal Processing · no summary yetYu Zhang et al.2022 · 180 citations
  5. Specaugment on Large Scale Datasets ↗IEEE International Conference on Acoustics Speech and Signal Processing · no summary yetDaniel Park et al.2020 · 154 citations
  6. SpeechStew: Simply Mix All Available Speech Recognition Data to Train One Large Neural Network ↗arXiv (Cornell University) · no summary yetWilliam Chan et al.2021 · 92 citations
  7. Noise2Music: Text-conditioned Music Generation with Diffusion Models ↗arXiv (Cornell University) · no summary yetQingqing Huang et al.2023 · 66 citations
  8. Large-scale Language Model Rescoring on Long-form DataIEEE International Conference on Acoustics Speech and Signal ProcessingTongzhou Chen, Cyril Allauzen, Yinghui Huang, et al.2023 · 21 citations
  9. The Effect of Network Width on Stochastic Gradient Descent and Generalization: an Empirical Study ↗arXiv (Cornell University) · no summary yetDaniel Park et al.2019 · 13 citations
  10. G-Augment: Searching for the Meta-Structure of Data Augmentation Policies for ASR ↗IEEE Spoken Language Technology Workshop (SLT) · no summary yetGary Wang et al.2023 · 2 citations

 Co-authors here

9 co-authors with a page, the most papers together first.

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

 Cited by

Papers of our library cite their works 489 times (of the 149,439 papers whose bibliographies we read); 54 citing papers with a page here →.

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