An empirical study of smoothing techniques for language modeling

Chen, Stanley F.; Goodman, Joshua H

doi:10.1006/csla.1999.0128

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An empirical study of smoothing techniques for language modeling

Author(s): Stanley F. Chen , Joshua Goodman

Publication date Created: October 1999

Publication date (Print): October 1999

Journal: Computer Speech & Language

Publisher: Elsevier BV

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A maximum likelihood approach to continuous speech recognition.

Lalit R. Bahl, Frederick Jelinek, Robert L. Mercer (1983)

Speech recognition is formulated as a problem of maximum likelihood decoding. This formulation requires statistical models of the speech production process. In this paper, we describe a number of statistical models for use in speech recognition. We give special attention to determining the parameters for such models from sparse data. We also describe two decoding methods, one appropriate for constrained artificial languages and one appropriate for more realistic decoding tasks. To illustrate the usefulness of the methods described, we review a number of decoding results that have been obtained with them.