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Dong Yu

59 papers hereh-index 8246.6k citations576 works total

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

author position
  • first author1
  • middle author17
  • last author37

Across the 55 of 59 papers where every author was matched, so the position is known.

fields
  • cs.CL32
  • eess.AS16
  • cs.SD5
  • cs.LG4
  • cs.AI1
  • cs.CR1
same name
  • Dong Yu — 40 papers, h 29
  • Dong Yu — 29 papers
  • Dong Yu — 27 papers, h 15
  • Dong Yu — 21 papers, h 7
  • Dong Yu — 19 papers, h 13
  • Dong Yu — 18 papers, h 16

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
20182025
most citedEnd-to-End Multi-Channel Speech Separation

80 citations · 190 across the 39 of their papers we have counts for

collaborators
Showing cs.LGShow all

4 papers · 1 filter

cs.LG2019★ 4 cited

A Unified Framework for Speech Separation

Fahimeh Bahmaninezhad, Shi-Xiong Zhang, Yong Xu +3

Speech separation refers to extracting each individual speech source in a given mixed signal. Recent advancements in speech separation and ongoing research in this area, have made…

cs.LG2019

Learning discriminative features in sequence training without requiring framewise labelled data

Jun Wang, Dan Su, Jie Chen +4

In this work, we try to answer two questions: Can deeply learned features with discriminative power benefit an ASR system's robustness to acoustic variability? And how to learn the…

cs.LG2019

Cross-lingual Knowledge Graph Alignment via Graph Matching Neural Network

Kun Xu, Liwei Wang, Mo Yu +4

Previous cross-lingual knowledge graph (KG) alignment studies rely on entity embeddings derived only from monolingual KG structural information, which may fail at matching entities…

cs.LG2018

A Comparison of Lattice-free Discriminative Training Criteria for Purely Sequence-Trained Neural Network Acoustic Models

Chao Weng, Dong Yu

In this work, three lattice-free (LF) discriminative training criteria for purely sequence-trained neural network acoustic models are compared on LVCSR tasks, namely maximum mutual…

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