What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Speech recognition
- Natural language processing
Artificial intelligence, Speech recognition, Natural language processing, Language model and Pattern recognition are his primary areas of study.
His Artificial intelligence research focuses on Vocabulary and how it relates to Context.
Jean-Luc Gauvain combines subjects such as Phone and Cluster analysis with his study of Speech recognition.
His Natural language processing study incorporates themes from Test, Speech corpus and Dictation.
His Language model research is multidisciplinary, relying on both Artificial neural network, Text corpus, Word and Cache language model.
His work in Pattern recognition tackles topics such as Maximum a posteriori estimation which are related to areas like Estimation theory, Feature vector, Transcription and TIMIT.
His most cited work include:
- Maximum a posteriori estimation for multivariate Gaussian mixture observations of Markov chains (1991 citations)
- Neural Probabilistic Language Models (399 citations)
- The LIMSI Broadcast News transcription system (382 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Speech recognition, Artificial intelligence, Natural language processing, Language model and Word error rate.
His Speech recognition study integrates concerns from other disciplines, such as Transcription and Phone.
His Artificial intelligence research is multidisciplinary, incorporating elements of Vocabulary and Pattern recognition.
His research integrates issues of Cluster analysis and Maximum a posteriori estimation in his study of Pattern recognition.
His study in Natural language processing is interdisciplinary in nature, drawing from both Speech corpus, Speech processing and Mandarin Chinese.
His Language model study deals with Training set intersecting with Machine learning.
He most often published in these fields:
- Speech recognition (72.73%)
- Artificial intelligence (68.64%)
- Natural language processing (51.36%)
What were the highlights of his more recent work (between 2008-2020)?
- Speech recognition (72.73%)
- Artificial intelligence (68.64%)
- Natural language processing (51.36%)
In recent papers he was focusing on the following fields of study:
Jean-Luc Gauvain mainly focuses on Speech recognition, Artificial intelligence, Natural language processing, Language model and Word error rate.
Jean-Luc Gauvain has included themes like Transcription, Word and Machine translation in his Speech recognition study.
His biological study spans a wide range of topics, including Vocabulary and Pattern recognition.
His Pattern recognition research incorporates themes from Posterior probability and Maximum a posteriori estimation.
His Natural language processing research incorporates elements of Speech transcription, Decoding methods, Conversation and Mandarin Chinese.
His study explores the link between Language model and topics such as Context that cross with problems in Lithuanian, VoxForge, Speech corpus and Conversational speech.
Between 2008 and 2020, his most popular works were:
- Structured Output Layer neural network language model (125 citations)
- Comparison of feedforward and recurrent neural network language models (85 citations)
- Structured Output Layer Neural Network Language Models for Speech Recognition (55 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Speech recognition
- Statistics
Jean-Luc Gauvain focuses on Artificial intelligence, Speech recognition, Natural language processing, Artificial neural network and Language model.
The various areas that Jean-Luc Gauvain examines in his Artificial intelligence study include Transcription and Pattern recognition.
The study incorporates disciplines such as Feature extraction and Word in addition to Speech recognition.
His Natural language processing research is multidisciplinary, incorporating perspectives in Transcription and Decoding methods.
His research in Artificial neural network intersects with topics in Vocabulary and Word error rate.
The Language model study which covers Recurrent neural nets that intersects with Intelligent control, Feed forward, Normalization, Deep learning and Types of artificial neural networks.
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