H-Index & Metrics Best Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science D-index 45 Citations 26,122 137 World Ranking 3611 National Ranking 1860

Research.com Recognitions

Awards & Achievements

2018 - IEEE Fellow For contributions to acoustic models for automatic speech recognition

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Speech recognition

His primary scientific interests are in Speech recognition, Artificial intelligence, Artificial neural network, Hidden Markov model and Pattern recognition. His biological study spans a wide range of topics, including Feature and Robustness. His work on Machine learning expands to the thematically related Artificial intelligence.

His Artificial neural network research incorporates themes from FMLLR, Convolutional neural network and Bayes' theorem. The study incorporates disciplines such as Mixture model, Scripting language and Modular design in addition to Hidden Markov model. Many of his research projects under Pattern recognition are closely connected to Phone with Phone, tying the diverse disciplines of science together.

His most cited work include:

  • Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups (6052 citations)
  • Deep Neural Networks for Acoustic Modeling in Speech Recognition (1745 citations)
  • Deep convolutional neural networks for LVCSR (728 citations)

What are the main themes of his work throughout his whole career to date?

The scientist’s investigation covers issues in Speech recognition, Artificial intelligence, Artificial neural network, Natural language processing and Word error rate. Speech processing, Language model, Acoustic model, Voice activity detection and Transcription are the core of his Speech recognition study. The Artificial intelligence study combines topics in areas such as Machine learning and Pattern recognition.

With his scientific publications, his incorporates both Pattern recognition and Frame. In the subject of general Artificial neural network, his work in Time delay neural network and Deep neural networks is often linked to Training, thereby combining diverse domains of study. His work carried out in the field of Hidden Markov model brings together such families of science as Mixture model and Normalization.

He most often published in these fields:

  • Speech recognition (70.62%)
  • Artificial intelligence (61.87%)
  • Artificial neural network (30.00%)

What were the highlights of his more recent work (between 2017-2021)?

  • Speech recognition (70.62%)
  • Artificial intelligence (61.87%)
  • Deep learning (10.63%)

In recent papers he was focusing on the following fields of study:

His primary areas of study are Speech recognition, Artificial intelligence, Deep learning, Spoken language and Word error rate. His Speech recognition study focuses on Language model in particular. His study on Acoustic model is often connected to Space as part of broader study in Artificial intelligence.

His work deals with themes such as Matrix, Supercomputer, Computer engineering and Task, which intersect with Deep learning. His Word error rate study incorporates themes from Contrast, Set and Test set. His Artificial neural network research is multidisciplinary, relying on both Mutual information and Constant.

Between 2017 and 2021, his most popular works were:

  • Estimating Information Flow in Deep Neural Networks (50 citations)
  • AVLnet: Learning Audio-Visual Language Representations from Instructional Videos. (16 citations)
  • Single headed attention based sequence-to-sequence model for state-of-the-art results on Switchboard (16 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Machine learning
  • Programming language

Brian Kingsbury mostly deals with Speech recognition, Artificial intelligence, Deep neural networks, Stability and Face. His study in the fields of Word error rate under the domain of Speech recognition overlaps with other disciplines such as Bit. His research integrates issues of Contrast, Set, Noise and Benchmark in his study of Word error rate.

Artificial intelligence is closely attributed to Machine learning in his study. His Deep neural networks research includes elements of Information flow, Backpropagation, Data mining and Minification. He combines subjects such as Luminance, Task analysis and Pattern recognition with his study of Stability.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups

G. Hinton;Li Deng;Dong Yu;G. E. Dahl.
IEEE Signal Processing Magazine (2012)

8695 Citations

Deep Neural Networks for Acoustic Modeling in Speech Recognition

Geoffrey Hinton;Li Deng;Dong Yu;George Dahl.
IEEE Signal Processing Magazine (2012)

5940 Citations

Deep convolutional neural networks for LVCSR

Tara N. Sainath;Abdel-rahman Mohamed;Brian Kingsbury;Bhuvana Ramabhadran.
international conference on acoustics, speech, and signal processing (2013)

1127 Citations

New types of deep neural network learning for speech recognition and related applications: an overview

Li Deng;Geoffrey Hinton;Brian Kingsbury.
international conference on acoustics, speech, and signal processing (2013)

828 Citations

Low-rank matrix factorization for Deep Neural Network training with high-dimensional output targets

Tara N. Sainath;Brian Kingsbury;Vikas Sindhwani;Ebru Arisoy.
international conference on acoustics, speech, and signal processing (2013)

509 Citations

Boosted MMI for model and feature-space discriminative training

D. Povey;D. Kanevsky;B. Kingsbury;B. Ramabhadran.
international conference on acoustics, speech, and signal processing (2008)

453 Citations

Deep Convolutional Neural Networks for Large-scale Speech Tasks

Tara N. Sainath;Brian Kingsbury;George Saon;Hagen Soltau.
Neural Networks (2015)

428 Citations

fMPE: discriminatively trained features for speech recognition

D. Povey;B. Kingsbury;L. Mangu;G. Saon.
international conference on acoustics, speech, and signal processing (2005)

370 Citations

Lattice-based optimization of sequence classification criteria for neural-network acoustic modeling

Brian Kingsbury.
international conference on acoustics, speech, and signal processing (2009)

323 Citations

Data augmentation for deep neural network acoustic modeling

Xiaodong Cui;Vaibhava Goel;Brian Kingsbury.
IEEE Transactions on Audio, Speech, and Language Processing (2015)

320 Citations

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Best Scientists Citing Brian Kingsbury

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Jinyu Li

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Mark J. F. Gales

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