2004 - IEEE Fellow For contributions to statistical methods for human language processing.
His primary scientific interests are in Artificial intelligence, Speech recognition, Natural language processing, Language model and Pattern recognition. The Word research Jerome R. Bellegarda does as part of his general Artificial intelligence study is frequently linked to other disciplines of science, such as Set and Key, therefore creating a link between diverse domains of science. In the field of Speech recognition, his study on Speech processing and Speech synthesis overlaps with subjects such as Front and back ends and Sequence.
He regularly ties together related areas like Machine learning in his Natural language processing studies. His research in Language model intersects with topics in Latent semantic analysis, Context, Representation and Vocabulary. His Pattern recognition study combines topics from a wide range of disciplines, such as Vector space and Probabilistic latent semantic analysis.
Jerome R. Bellegarda mostly deals with Artificial intelligence, Natural language processing, Speech recognition, Word and Latent semantic analysis. His Artificial intelligence research integrates issues from Context and Pattern recognition. His work in the fields of Natural language processing, such as Natural language, Language identification and Latent semantic mapping, intersects with other areas such as Sequence.
His work investigates the relationship between Speech recognition and topics such as Pronunciation that intersect with problems in Orthographic projection, Transcription, Proper noun and Space. The various areas that Jerome R. Bellegarda examines in his Word study include Acoustic model, Representation and Inference. His Latent semantic analysis research focuses on Probabilistic latent semantic analysis and how it connects with Document-term matrix.
His primary areas of study are Artificial intelligence, Natural language processing, Real-time computing, Handwriting recognition and Generative grammar. His Artificial intelligence research focuses on Handwriting and Deep learning. Jerome R. Bellegarda interconnects Transcription, Cursive and Rendering in the investigation of issues within Handwriting.
As part of one scientific family, Jerome R. Bellegarda deals mainly with the area of Natural language processing, narrowing it down to issues related to the Training set, and often Word. His Handwriting recognition study combines topics in areas such as Machine learning, Speech recognition and Chinese characters. His Generative grammar research is multidisciplinary, relying on both Adversarial system, Theoretical computer science and Data set.
His primary areas of investigation include Artificial intelligence, Deep learning, Handwriting, Gesture and Human–computer interaction. His research on Artificial intelligence frequently connects to adjacent areas such as Machine learning. Jerome R. Bellegarda combines subjects such as Transcription, Rendering and Natural language processing with his study of Deep learning.
Jerome R. Bellegarda performs multidisciplinary study on Handwriting and Guard in his works. His Gesture study overlaps with User interface and Content.
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.
Statistical language model adaptation: review and perspectives
Jerome R Bellegarda.
Speech Communication (2004)
Method, Device, and Graphical User Interface Providing Word Recommendations for Text Input
Kenneth Kocienda;Greg Christie;Bas Ording;Scott Forstall.
Large-vocabulary speech recognition using an integrated syntactic and semantic statistical language model
Jerome R. Bellegarda.
Method and system for providing word recommendations for text input
Greg Christie;Bas Ording;Scott Forstall;Kenneth Kocienda.
Method and system for deriving a large-span semantic language model for large-vocabulary recognition systems
Jerome R. Bellegarda;Yen-Lu Chow.
Fast, language-independent method for user authentication by voice
Jerome R. Bellegarda;Kim E. A. Silverman.
Method and apparatus for command recognition using data-driven semantic inference
Jerome R. Bellegarda;Kim E. A. Silverman.
Multi-command single utterance input method
Sabatelli Alessandro;Gruber Thomas R;Saddler Harry J;Bellegarda Jerome Rene.
Method and apparatus for improved duration modeling of phonemes
Jerome R. Bellegarda;Kim Silverman.
Journal of the Acoustical Society of America (2002)
Exploiting both local and global constraints for multi-span statistical language modeling
international conference on acoustics speech and signal processing (1998)
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below: