Wei-Ning Hsu

2 papers on this site · 2021

Wei-Ning Hsu has 2 papers on this site, in 2021, most of them on Language models & pretraining, Speech & spoken language; often with Abdelrahman Mohamed, Benjamin Bolte, Kushal Lakhotia.

OpenAlex · ORCID · h-index 33 · 115 works · 7 221 citations OpenAlex, 2026-10-14

2 works on this site · 1 035 citations in our library · 4 more works in our library

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

Johns Hopkins University · Baltimore, United States — 2021–2026 on 3 papers
Meta (United States) · Menlo Park, United States — 2020–2025 on 29 papers
MIT Computer Science and Artificial Intelligence Laboratory · Cambridge, United States — 2016–2023 on 13 papers
Meta (Israel) · Tel Aviv, Israel — 2021–2023 on 4 papers
Meta (United Kingdom) · London, United Kingdom — 2021–2023 on 2 papers
École des hautes études en sciences sociales · Paris, France — 2021–2022 on 4 papers
Bar-Ilan University · Ramat Gan, Israel — 2021–2022 on 2 papers
Massachusetts Institute of Technology · Cambridge, United States — 2016–2021 on 25 papers
National Taiwan University · Taipei, Taiwan — 2015 on 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 5 more institutions on single bylines are not listed.

Topics

Language models & pretraining1 Speech & spoken language1

Papers

The most cited first (OpenAlex and our library).

HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units (2021 · 3 441 citations)
Unsupervised speech representation learning is harder than in text or vision for three reasons. Each utterance contains many sounds, there is no ready-made inventory of discrete units, and unit boundaries are unknown. HuBERT (Hidden-Unit BERT) handles this by first clustering acoustic frames offline, starting with…
On Generative Spoken Language Modeling from Raw Audio (2021 · 165 citations)
The paper introduces Generative Spoken Language Modeling (GSLM): learning both the sound system and the linguistic structure of a language directly from raw audio, without transcripts or labels.

Also in our library

4 more works of theirs are in our library without a page here yet; they will appear as we publish them.

Co-authors here

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

Cited by

Papers of our library cite their works 1 035 times (of the 140 884 papers whose bibliographies we read); 57 citing papers with a page here →.

This page in Markdown → https://md.research.elamur.ai/free/en/authors/wei-ning-hsu/

🧑‍🔬 Wei-Ning Hsu