What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
His scientific interests lie mostly in Speech recognition, Artificial intelligence, Pattern recognition, Cluster analysis and Source separation.
Jonathan Le Roux has included themes like Speech enhancement, Recurrent neural network and Communication channel in his Speech recognition study.
His work on Deep learning and Inference as part of general Artificial intelligence research is often related to Matrix decomposition, thus linking different fields of science.
His Pattern recognition study focuses on Discriminative model in particular.
His Cluster analysis research focuses on Network architecture and how it relates to Correlation clustering.
His Source separation research includes themes of Speaker recognition, Speaker diarisation and Reverberation.
His most cited work include:
- Deep clustering: Discriminative embeddings for segmentation and separation (691 citations)
- Phase-sensitive and recognition-boosted speech separation using deep recurrent neural networks (402 citations)
- Speech Enhancement with LSTM Recurrent Neural Networks and its Application to Noise-Robust ASR (363 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Speech recognition, Artificial intelligence, Source separation, Pattern recognition and Deep learning.
His Speech recognition study combines topics in areas such as Artificial neural network, Recurrent neural network, Speech enhancement and End-to-end principle.
His work in the fields of Cluster analysis, Discriminative model and Inference overlaps with other areas such as Non-negative matrix factorization and Matrix decomposition.
Jonathan Le Roux focuses mostly in the field of Source separation, narrowing it down to topics relating to Spectrogram and, in certain cases, Audio signal.
His work on Hidden Markov model, Classifier and Feature extraction is typically connected to Noise as part of general Pattern recognition study, connecting several disciplines of science.
The study incorporates disciplines such as Network architecture, Segmentation, Image segmentation and Time–frequency analysis in addition to Deep learning.
He most often published in these fields:
- Speech recognition (59.15%)
- Artificial intelligence (45.77%)
- Source separation (30.99%)
What were the highlights of his more recent work (between 2019-2021)?
- Speech recognition (59.15%)
- Transformer (7.04%)
- Artificial intelligence (45.77%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Speech recognition, Transformer, Artificial intelligence, Source separation and Artificial neural network.
His study in the field of Utterance is also linked to topics like Masking.
Jonathan Le Roux has researched Transformer in several fields, including Recurrent neural network and Reduction.
His Artificial intelligence study incorporates themes from Speech enhancement and Machine learning.
His Source separation study is related to the wider topic of Algorithm.
His study in Deep learning is interdisciplinary in nature, drawing from both Network architecture, Noise, Reverberation and Classifier, Pattern recognition.
Between 2019 and 2021, his most popular works were:
- WHAMR!: Noisy and Reverberant Single-Channel Speech Separation (21 citations)
- End-To-End Multi-Speaker Speech Recognition With Transformer (18 citations)
- Streaming automatic speech recognition with the transformer model (14 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
His primary scientific interests are in Artificial intelligence, Transformer, Speech recognition, Deep learning and Source separation.
Many of his studies on Artificial intelligence involve topics that are commonly interrelated, such as Algorithm.
His work is dedicated to discovering how Transformer, Recurrent neural network are connected with End-to-end principle and Utterance and other disciplines.
His Speech recognition research is multidisciplinary, incorporating perspectives in Speech enhancement, Encoder, Noise and Reverberation.
His studies in Deep learning integrate themes in fields like Selection, Cocktail party effect, Channel, Computer audition and Gradient descent.
His Source separation study integrates concerns from other disciplines, such as Network architecture and Classifier, Pattern recognition.
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.
