William Byrne mostly deals with Artificial intelligence, Speech recognition, Natural language processing, Machine translation and Translation. A large part of his Artificial intelligence studies is devoted to Bayes' theorem. His study in Speech recognition is interdisciplinary in nature, drawing from both Normalization and Segmentation.
The study incorporates disciplines such as Czech and Vocabulary in addition to Natural language processing. His work carried out in the field of Translation brings together such families of science as Data mining and Pruning. His study in Transfer-based machine translation is interdisciplinary in nature, drawing from both Bitext word alignment and Word.
His scientific interests lie mostly in Artificial intelligence, Speech recognition, Natural language processing, Hidden Markov model and Machine translation. His Artificial intelligence study combines topics from a wide range of disciplines, such as Decoding methods and Pattern recognition. His Speech recognition research is multidisciplinary, incorporating perspectives in Vocabulary and Discriminative model.
The Natural language processing study combines topics in areas such as Czech, Speech corpus and Translation. As part of one scientific family, William Byrne deals mainly with the area of Hidden Markov model, narrowing it down to issues related to the Estimation theory, and often Expectation–maximization algorithm. The study incorporates disciplines such as Sentence and Word in addition to Machine translation.
William Byrne focuses on Artificial intelligence, Speech recognition, Natural language processing, Machine translation and Phrase. William Byrne studies Artificial intelligence, namely Translation. His study in Speech synthesis, Hidden Markov model and Speaker recognition are all subfields of Speech recognition.
William Byrne does research in Natural language processing, focusing on Rule-based machine translation specifically. His work on Language translation as part of general Machine translation study is frequently linked to Simple, therefore connecting diverse disciplines of science. The various areas that William Byrne examines in his Phrase study include Language model, Theoretical computer science, Generative model and NIST.
His primary areas of study are Artificial intelligence, Natural language processing, Speech recognition, Machine translation and Translation. William Byrne is involved in the study of Natural language processing that focuses on Rule-based machine translation in particular. His work is connected to Hidden Markov model, Speaker recognition and Speech synthesis, as a part of Speech recognition.
The concepts of his Machine translation study are interwoven with issues in Decoding methods, Word and Bayes' theorem. His Bayes' theorem study integrates concerns from other disciplines, such as Language model and Synchronous context-free grammar. William Byrne interconnects Machine learning, Pruning and Phrase in the investigation of issues within Translation.
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Minimum Bayes-risk decoding for statistical machine translation
Shankar Kumar;William J. Byrne.
north american chapter of the association for computational linguistics (2004)
Minimum bayes-risk automatic speech recognition
Vaibhava Goel;William J Byrne.
Computer Speech & Language (2000)
Stochastic pronunciation modelling from hand-labelled phonetic corpora
Michael Riley;William Byrne;Michael Finke;Sanjeev Khudanpur.
Speech Communication (1999)
Convergence Theorems for Generalized Alternating Minimization Procedures
Asela Gunawardana;William Byrne.
Journal of Machine Learning Research (2005)
Automatic recognition of spontaneous speech for access to multilingual oral history archives
W. Byrne;D. Doermann;M. Franz;S. Gustman.
IEEE Transactions on Speech and Audio Processing (2004)
Towards language independent acoustic modeling
W. Byrne;P. Beyerlein;J.M. Huerta;S. Khudanpur.
international conference on acoustics, speech, and signal processing (2000)
Consensus Network Decoding for Statistical Machine Translation System Combination
K. C. Sim;W. J. Byrne;M. J. F. Gales;H. Sahbi.
international conference on acoustics, speech, and signal processing (2007)
HMM Word and Phrase Alignment for Statistical Machine Translation
Yonggang Deng;W. Byrne.
IEEE Transactions on Audio, Speech, and Language Processing (2008)
Local Phrase Reordering Models for Statistical Machine Translation
Shankar Kumar;William Byrne.
empirical methods in natural language processing (2005)
Alternating minimization and Boltzmann machine learning
IEEE Transactions on Neural Networks (1992)
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