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Robert Leroy Mercer
Researcher at IBM
Publications - 109
Citations - 22448
Robert Leroy Mercer is an academic researcher from IBM. The author has contributed to research in topics: Word (computer architecture) & Markov model. The author has an hindex of 54, co-authored 109 publications receiving 21898 citations. Previous affiliations of Robert Leroy Mercer include Renaissance Technologies.
Papers
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Journal Article
The mathematics of statistical machine translation: parameter estimation
TL;DR: The authors describe a series of five statistical models of the translation process and give algorithms for estimating the parameters of these models given a set of pairs of sentences that are translations of one another.
Journal ArticleDOI
Class-based n -gram models of natural language
Peter Fitzhugh Brown,Peter Vincent Desouza,Robert Leroy Mercer,Vincent J. Della Pietra,Jenifer C. Lai +4 more
TL;DR: This work addresses the problem of predicting a word from previous words in a sample of text and discusses n-gram models based on classes of words, finding that these models are able to extract classes that have the flavor of either syntactically based groupings or semanticallybased groupings, depending on the nature of the underlying statistics.
Journal ArticleDOI
A statistical approach to machine translation
Peter Fitzhugh Brown,John Cocke,Stephen A. Della Pietra,Vincent J. Della Pietra,F. Jelinek,John Lafferty,Robert Leroy Mercer,Paul S. Roossin +7 more
TL;DR: The application of the statistical approach to translation from French to English and preliminary results are described and the results are given.
Journal ArticleDOI
A Maximum Likelihood Approach to Continuous Speech Recognition
TL;DR: This paper describes a number of statistical models for use in speech recognition, with special attention to determining the parameters for such models from sparse data, and describes two decoding methods appropriate for constrained artificial languages and one appropriate for more realistic decoding tasks.
Proceedings ArticleDOI
Maximum mutual information estimation of hidden Markov model parameters for speech recognition
TL;DR: A method for estimating the parameters of hidden Markov models of speech is described and recognition results are presented comparing this method with maximum likelihood estimation.