Dong Yu

45.1k citations
375 papers · 26.9k · 19 hit papers · h-index 61

Impact in

    • Speech and Audio Processing
    • Music and Audio Processing
    • Speech Recognition and Synthesis
    • Natural Language Processing Techniques
    • Topic Modeling
    • Anomaly Detection Techniques and Applications
    • Neural Networks and Applications

Papers in

    • Speech Recognition and Synthesis 228
    • Natural Language Processing Techniques 104
    • Topic Modeling 93
    • Speech and dialogue systems 37
    • Speech and Audio Processing 200
    • Music and Audio Processing 167

Dong Yu

354 papers receiving 24.8k citations

Dong Yu's Hit Papers

Diffsound: Discrete Diffusion Model for Text-to-Sound Generation 2023 · 108 citations
1080+4+9Years since publication2.0k4.0k6.0k

Peers

Dong Yu
Comparison fields: 5 of 217
  • Signal Processing 12.2k
  • Artificial Intelligence 17.8k
  • Computer Vision and Pattern Recognition 5.1k
  • Computational Mathematics 88
  • Experimental and Cognitive Psychology 1.1k
Replace Li Deng with:
Li Deng United States
Abdelrahman Mohamed United States
Andrew Senior United States
Tara N. Sainath United States
Alex Graves United States
George E. Dahl United States
Quoc V. Le United States
Brian Kingsbury United States
Nitish Srivastava United States
Zoubin Ghahramani United Kingdom
Dong Yu relative to Li Deng United States Li Deng's profile →
Citations per field
00.5×1.5×
Li Deng · 1×
Citations per year

Countries citing papers authored by Dong Yu

Since Specialization
Citations

This map shows the geographic impact of Dong Yu's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Dong Yu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dong Yu more than expected).

Fields of papers citing papers by Dong Yu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Dong Yu. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Dong Yu. The network helps show where Dong Yu may publish in the future.

Co-authors

The 25 scholars most cited alongside Dong Yu, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Dong Yu Line = papers co-authored together Dong Yu links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 375 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups
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20126614
2
Deep Learning: Methods and Applications
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20142335
3
Convolutional Neural Networks for Speech Recognition
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20141566
4
Deep Neural Networks for Acoustic Modeling in Speech Recognition
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20121169
5
Conversational speech transcription using context-dependent deep neural networks
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2011599
6
Permutation invariant training of deep models for speaker-independent multi-talker speech separation
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2017541
7
Multitalker Speech Separation With Utterance-Level Permutation Invariant Training of Deep Recurrent Neural Networks
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2017533
8
Speech emotion recognition using deep neural network and extreme learning machine
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2014524
9
Recent advances in deep learning for speech research at Microsoft
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2013510
10
Feature engineering in Context-Dependent Deep Neural Networks for conversational speech transcription
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2011482
11
An investigation of deep neural networks for noise robust speech recognition
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2013460
12
1-bit stochastic gradient descent and its application to data-parallel distributed training of speech DNNs
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2014439
13
Cross-language knowledge transfer using multilingual deep neural network with shared hidden layers
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2013400
14
Deep Learning and Its Applications to Signal and Information Processing [Exploratory DSP
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2010336
15
KL-divergence regularized deep neural network adaptation for improved large vocabulary speech recognition
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2013300
16 2014271
17
Large-scale malware classification using random projections and neural networks
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2013270
18 2013256
19
Binary coding of speech spectrograms using a deep auto-encoder
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2010243
20 2019243

About Dong Yu

Dong Yu is a scholar working on Artificial Intelligence, Signal Processing, Computer Vision and Pattern Recognition, Computational Mechanics and Information Systems, having authored 375 papers that have together received 26.9k indexed citations. Recurring topics across this work include Speech Recognition and Synthesis (228 papers), Speech and Audio Processing (200 papers), Music and Audio Processing (167 papers), Natural Language Processing Techniques (104 papers), Topic Modeling (93 papers), Speech and dialogue systems (37 papers), Advanced Adaptive Filtering Techniques (21 papers) and Multimodal Machine Learning Applications (21 papers). The work is most often cited by research in Signal Processing (12.2k citations), Artificial Intelligence (17.8k citations), Computer Vision and Pattern Recognition (5.1k citations), Computational Mathematics (88 citations) and Experimental and Cognitive Psychology (1.1k citations). Dong Yu has collaborated with scholars based in United States, China and United Kingdom. Frequent co-authors include Li Deng, Abdelrahman Mohamed, George E. Dahl, Geoffrey E. Hinton, Patrick Nguyen, Brian Kingsbury, Andrew Senior, Vincent Vanhoucke, Tara N. Sainath and Navdeep Jaitly. Their work appears in journals such as IEEE/ACM Transactions on Audio Speech and Language Processing, IEEE Signal Processing Magazine, IEEE Transactions on Audio Speech and Language Processing, Computer Speech & Language and IEEE Journal of Selected Topics in Signal Processing.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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