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.
