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Weiran Wang

28 papers hereh-index 11350 citations20 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author4
  • middle author15
  • last author6

Across the 25 of 28 papers where every author was matched, so the position is known.

fields
  • cs.CL11
  • cs.LG6
  • eess.AS6
  • cs.SD2
  • astro-ph.GA1
  • astro-ph.SR1
same name
  • Weiran Wang — 42 papers, h 27
  • Weiran Wang — 4 papers, h 3
  • Weiran Wang — 3 papers, h 3
  • Weiran Wang — 1 paper, h 4
  • Weiran Wang — 1 paper, h 5
  • Weiran Wang — 1 paper

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20212026
most citedOptical Flux and Colour Variability of Blazars in the ZTF Survey

35 citations · 59 across the 25 of their papers we have counts for

collaborators
Showing 2022 · cs.CLShow all

4 papers · 2 filters

cs.CL2022★ 1 cited

JOIST: A Joint Speech and Text Streaming Model For ASR

Tara N. Sainath, Rohit Prabhavalkar, Ankur Bapna +6

We present JOIST, an algorithm to train a streaming, cascaded, encoder end-to-end (E2E) model with both speech-text paired inputs, and text-only unpaired inputs. Unlike previous wo…

cs.CL2022

Improving Deliberation by Text-Only and Semi-Supervised Training

Ke Hu, Tara N. Sainath, Yanzhang He +4

Text-only and semi-supervised training based on audio-only data has gained popularity recently due to the wide availability of unlabeled text and speech data. In this work, we prop…

cs.CL2022

Streaming Align-Refine for Non-autoregressive Deliberation

Weiran Wang, Ke Hu, Tara N. Sainath

We propose a streaming non-autoregressive (non-AR) decoding algorithm to deliberate the hypothesis alignment of a streaming RNN-T model. Our algorithm facilitates a simple greedy d…

cs.CL2022

Improving Rare Word Recognition with LM-aware MWER Training

Weiran Wang, Tongzhou Chen, Tara N. Sainath +10

Language models (LMs) significantly improve the recognition accuracy of end-to-end (E2E) models on words rarely seen during training, when used in either the shallow fusion or the…

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