Brian Kingsbury

21.8k total citations · 6 hit papers
137 papers, 14.1k citations indexed

About

Brian Kingsbury is a scholar working on Artificial Intelligence, Signal Processing and Computer Vision and Pattern Recognition. According to data from OpenAlex, Brian Kingsbury has authored 137 papers receiving a total of 14.1k indexed citations (citations by other indexed papers that have themselves been cited), including 126 papers in Artificial Intelligence, 84 papers in Signal Processing and 15 papers in Computer Vision and Pattern Recognition. Recurrent topics in Brian Kingsbury's work include Speech Recognition and Synthesis (111 papers), Speech and Audio Processing (73 papers) and Music and Audio Processing (60 papers). Brian Kingsbury is often cited by papers focused on Speech Recognition and Synthesis (111 papers), Speech and Audio Processing (73 papers) and Music and Audio Processing (60 papers). Brian Kingsbury collaborates with scholars based in United States, Israel and Canada. Brian Kingsbury's co-authors include Tara N. Sainath, Abdelrahman Mohamed, Li Deng, Geoffrey E. Hinton, George E. Dahl, Vincent Vanhoucke, Patrick Nguyen, Navdeep Jaitly, Dong Yu and Andrew Senior and has published in prestigious journals such as The Journal of the Acoustical Society of America, Computer and IEEE Signal Processing Magazine.

In The Last Decade

Brian Kingsbury

133 papers receiving 12.9k citations

Hit Papers

Deep Neural Networks for ... 2012 2026 2016 2021 2012 2012 2013 2013 2014 2.0k 4.0k 6.0k
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups
    2012 6.6k citations Geoffrey E. Hinton, Li Deng et al. IEEE Signal Processing Magazine profile →
  2. Deep Neural Networks for Acoustic Modeling in Speech Recognition
    2012 1.2k citations Geoffrey E. Hinton, Li Deng et al. IEEE Signal Processing Magazine profile →
  3. New types of deep neural network learning for speech recognition and related applications: an overview
    2013 738 citations Li Deng, Geoffrey E. Hinton et al. profile →
  4. Deep convolutional neural networks for LVCSR
    2013 723 citations Tara N. Sainath, Abdelrahman Mohamed et al. profile →
  5. Deep Convolutional Neural Networks for Large-scale Speech Tasks
    2014 364 citations Tara N. Sainath, Brian Kingsbury et al. profile →
  6. Low-rank matrix factorization for Deep Neural Network training with high-dimensional output targets
    2013 344 citations Tara N. Sainath, Brian Kingsbury et al. profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Brian Kingsbury United States 38 9.3k 6.0k 2.7k 1.1k 559 137 14.1k
Tara N. Sainath United States 48 11.5k 1.2× 7.6k 1.3× 3.2k 1.2× 1.4k 1.2× 687 1.2× 180 17.5k
Navdeep Jaitly United States 34 10.1k 1.1× 5.8k 1.0× 2.8k 1.0× 1.1k 1.0× 492 0.9× 62 16.7k
George E. Dahl United States 21 9.8k 1.1× 5.5k 0.9× 3.8k 1.4× 1.5k 1.3× 655 1.2× 25 17.6k
Patrick Nguyen United States 17 5.7k 0.6× 3.5k 0.6× 1.7k 0.6× 813 0.7× 365 0.7× 55 8.9k
Oriol Vinyals United States 33 9.1k 1.0× 3.3k 0.5× 7.8k 2.9× 854 0.8× 437 0.8× 74 16.4k
Sanjeev Khudanpur United States 45 14.8k 1.6× 8.6k 1.4× 2.0k 0.8× 451 0.4× 340 0.6× 269 17.9k
Mike Schuster United States 13 6.1k 0.7× 2.3k 0.4× 2.4k 0.9× 676 0.6× 333 0.6× 26 10.2k
Abdelrahman Mohamed United States 29 13.1k 1.4× 7.7k 1.3× 4.5k 1.7× 2.0k 1.8× 1.1k 1.9× 52 21.6k
Biing‐Hwang Juang United States 37 8.5k 0.9× 6.7k 1.1× 3.6k 1.3× 3.1k 2.7× 688 1.2× 184 15.7k
Mohamed S. Kamel Canada 44 5.0k 0.5× 2.6k 0.4× 3.6k 1.3× 1.1k 0.9× 436 0.8× 404 11.2k

Countries citing papers authored by Brian Kingsbury

Since Specialization
Citations

This map shows the geographic impact of Brian Kingsbury'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 Brian Kingsbury with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Brian Kingsbury more than expected).

Fields of papers citing papers by Brian Kingsbury

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Brian Kingsbury. 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 Brian Kingsbury. The network helps show where Brian Kingsbury may publish in the future.

Co-authorship network of co-authors of Brian Kingsbury

This figure shows the co-authorship network connecting the top 25 collaborators of Brian Kingsbury. A scholar is included among the top collaborators of Brian Kingsbury based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Brian Kingsbury. Brian Kingsbury is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
2.
Cui, Xiaodong, George Saon, & Brian Kingsbury. (2023). Improving RNN Transducer Acoustic Models for English Conversational Speech Recognition. 1299–1303. 1 indexed citations
3.
Thomas, Samuel, Hong-Kwang Jeff Kuo, George Saon, & Brian Kingsbury. (2023). Multi-Speaker Data Augmentation for Improved end-to-end Automatic Speech Recognition. 1–5.
4.
Chen, Brian, Andrew Rouditchenko, Hilde Kuehne, et al.. (2021). Multimodal Clustering Networks for Self-supervised Learning from Unlabeled Videos. 2021 IEEE/CVF International Conference on Computer Vision (ICCV). 7992–8001. 46 indexed citations
5.
Cui, Xiaodong, Brian Kingsbury, George Saon, David Haws, & Zoltán Tüske. (2021). Reducing Exposure Bias in Training Recurrent Neural Network Transducers. 1802–1806. 2 indexed citations
6.
Cong, Guojing, et al.. (2020). Fast Training of Deep Neural Networks for Speech Recognition. 6884–6888. 4 indexed citations
7.
Saon, George, Zoltán Tüske, Kartik Audhkhasi, & Brian Kingsbury. (2019). Sequence Noise Injected Training for End-to-end Speech Recognition. 6261–6265. 24 indexed citations
8.
Choromanska, Anna, et al.. (2018). Beyond Backprop: Alternating Minimization with co-Activation Memory.. arXiv (Cornell University). 4 indexed citations
9.
Sercu, Tom, George Saon, Jia Cui, et al.. (2017). Network architectures for multilingual speech representation learning. 5295–5299. 16 indexed citations
10.
Sercu, Tom, Christian Puhrsch, Brian Kingsbury, & Yann LeCun. (2016). Very deep multilingual convolutional neural networks for LVCSR. 4955–4959. 139 indexed citations
11.
Chen, Jie, et al.. (2016). Efficient one-vs-one kernel ridge regression for speech recognition. 2454–2458. 10 indexed citations
12.
Hinton, Geoffrey E., Li Deng, Dong Yu, et al.. (2012). Deep Neural Networks for Acoustic Modeling in Speech Recognition. IEEE Signal Processing Magazine. 29(6). 82–97. 1169 indexed citations breakdown →
13.
Sainath, Tara N., Brian Kingsbury, & Bhuvana Ramabhadran. (2012). Auto-encoder bottleneck features using deep belief networks. 4153–4156. 138 indexed citations
14.
Sha, Fei & Brian Kingsbury. (2012). Domain Adaptation in Machine Learning and Speech Processing. 2 indexed citations
15.
Povey, Daniel, Dimitri Kanevsky, Brian Kingsbury, et al.. (2008). Boosted MMI for model and feature-space discriminative training. Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing. 4057–4060. 250 indexed citations
16.
Asanović, Krste, James D. Beck, Brian Kingsbury, et al.. (2003). SPERT: a VLIW/SIMD microprocessor for artificial neural network computations. 1. 178–190. 1 indexed citations
17.
Kingsbury, Brian, George Saon, Lidia Mangu, M. Padmanabhan, & Ruhi Sarikaya. (2002). Robust speech recognition in Noisy Environments: The 2001 IBM spine evaluation system. IEEE International Conference on Acoustics Speech and Signal Processing. I–53. 37 indexed citations
18.
Morgan, Nelson, Daniel P. W. Ellis, Eric Fosler‐Lussier, Adam Janin, & Brian Kingsbury. (1999). Reducing errors by increasing the error rate: MLP Acoustic Modeling for Broadcast News Transcription. Columbia Academic Commons (Columbia University). 4 indexed citations
19.
Kingsbury, Brian & Nelson Morgan. (1998). Perceptually inspired signal processing strategies for robust speech recognition in reverberant environments. 40 indexed citations
20.
Asanović, Krste, et al.. (1996). T0: A Single-Chip Vector Microprocessor with Reconfigurable Pipelines. European Solid-State Circuits Conference. 344–347. 11 indexed citations

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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