  Speech processing requires very efficient methods and algorithms.
Finite-state transducers have been shown recently both to constitute a very
useful abstract model and to lead to highly efficient time and space algorithms
in this field. We present these methods and algorithms and illustrate them in
the case of speech recognition. In addition to classical techniques, we
describe many new algorithms such as minimization, global and local on-the-fly
determinization of weighted automata, and efficient composition of transducers.
These methods are currently used in large vocabulary speech recognition
systems. We then show how the same formalism and algorithms can be used in
text-to-speech applications and related areas of language processing such as
morphology, syntax, and local grammars, in a very efficient way. The tutorial
is self-contained and requires no specific computational or linguistic
knowledge other than classical results.
