2009 - ACM Senior Member
2000 - IEEE Fellow For contributions to the fields of statistical speech recognition and neural networks.
His primary areas of investigation include Speech recognition, Artificial intelligence, Hidden Markov model, Pattern recognition and Artificial neural network. Hervé Bourlard has included themes like Posterior probability and Robustness in his Speech recognition study. His Artificial intelligence research includes themes of Machine learning and Natural language processing.
His work deals with themes such as Connectionism, Speaker recognition, Vocabulary, Linear discriminant analysis and Multilayer perceptron, which intersect with Hidden Markov model. His research in Pattern recognition tackles topics such as Computer vision which are related to areas like Support vector machine. His Artificial neural network research includes elements of Language model and Context, Context model.
Hervé Bourlard mainly investigates Speech recognition, Artificial intelligence, Hidden Markov model, Pattern recognition and Artificial neural network. His work investigates the relationship between Speech recognition and topics such as Posterior probability that intersect with problems in Phone. His research in Artificial intelligence intersects with topics in Machine learning and Natural language processing.
His Hidden Markov model study combines topics from a wide range of disciplines, such as Context, Connectionism, Hybrid system, Markov model and Mixture model. His Pattern recognition research is multidisciplinary, incorporating perspectives in Cluster analysis and Robustness. He mostly deals with Time delay neural network in his studies of Artificial neural network.
Hervé Bourlard mostly deals with Speech recognition, Artificial intelligence, Artificial neural network, Pattern recognition and Hidden Markov model. His Speech recognition research incorporates elements of Context and Subspace topology. His studies deal with areas such as Machine learning and Natural language processing as well as Artificial intelligence.
The various areas that Hervé Bourlard examines in his Artificial neural network study include Template matching and Compressed sensing. His study looks at the relationship between Pattern recognition and topics such as Posterior probability, which overlap with k-nearest neighbors algorithm. The concepts of his Hidden Markov model study are interwoven with issues in Word error rate, Noise, Markov model, Mixture model and Principal component analysis.
Hervé Bourlard focuses on Speech recognition, Artificial intelligence, Artificial neural network, Pattern recognition and Hidden Markov model. His biological study spans a wide range of topics, including Connectionism and Dropout. The Artificial intelligence study combines topics in areas such as Social computing, Turn-taking and Natural language processing.
His work in Artificial neural network addresses subjects such as Template matching, which are connected to disciplines such as Feature extraction, Pattern matching and Feature vector. His work carried out in the field of Pattern recognition brings together such families of science as Subspace topology, Posterior probability, Distance matrix and Compressed sensing. His Hidden Markov model research integrates issues from Context and Machine learning.
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Connectionist Speech Recognition: A Hybrid Approach
Herve A. Bourlard;Nelson Morgan.
Auto-association by multilayer perceptrons and singular value decomposition
H. Bourlard;Y. Kamp.
Biological Cybernetics (1988)
Social signal processing
Alessandro Vinciarelli;Maja Pantic;Hervé Bourlard.
Image and Vision Computing (2009)
Links between Markov models and multilayer perceptrons
H. Bourlard;C.J. Wellekens.
IEEE Transactions on Pattern Analysis and Machine Intelligence (1990)
A mew ASR approach based on independent processing and recombination of partial frequency bands
H. Bourlard;S. Dupont.
international conference on spoken language processing (1996)
Text detection and recognition in images and video frames
Datong Chen;Jean-Marc Odobez;Hervé Bourlard.
Pattern Recognition (2004)
Microphone array post-filter based on noise field coherence
I.A. McCowan;H. Bourlard.
IEEE Transactions on Speech and Audio Processing (2003)
Connectionist probability estimators in HMM speech recognition
S. Renals;N. Morgan;H. Bourlard;M. Cohen.
IEEE Transactions on Speech and Audio Processing (1994)
Generalization and Parameter Estimation in Feedforward Nets: Some Experiments
N. Morgan;H. Bourlard.
neural information processing systems (1989)
Continuous speech recognition
N. Morgan;H. Bourlard.
IEEE Signal Processing Magazine (1995)
Profile was last updated on December 6th, 2021.
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