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.