A fully integrated approach to Speech-Input Language Translation in limited-domain applications is presented. The mapping from the input to the output language is modeled in terms of a finite state translation model which is learned from examples of input-output sentences of the task considered. This model is tightly integrated with standard acoustic-phonetic models of the input language and the resulting global model directly supplies, through Viterbi search, an optimal output-language sentence for each input -language utterance. Several extensions to this framework, recently developed to cope with the increasing difficulty of translation tasks, are reviewed. Finally, results for a task in the framework of hotel front-desk communication, with a vocabulary of about 700 words, are reported.

Probabilistic finite-state machines are used today in a variety of areas in pattern recognition, or in fields to which pattern recognition is linked: computational linguistics, machine learning, time series analysis, circuit testing, computational biology, speech recognition and machine translation are some of them. In part I of this paper we survey these generative objects and study their definitions and properties. In part II, we will study the relation of probabilistic finite-state automata with other well known devices that generate strings as hidden Markov models and n-grams, and provide theorems, algorithms and properties that represent a current state of the art of these objects.

Probabilistic finite-state machines are used today in a variety of areas in pattern recognition, or in fields to which pattern recognition is linked. In part I of this paper, we surveyed these objects and studied their properties. In this part II, we study the relations between probabilistic finite-state automata and other well known devices that generate strings like hidden Markov models and n- grams, and provide theorems, algorithms and properties that represent a current state of the art of these objects.

Language Understanding in limited domains is here approached as a problem of language translation in which the target language is a formal language rather than a natural one. Finite-state transducers are used to model the translation process. Furthermore, these models are automatically learned from training data consisting of pairs of natural-language/formal-language sentences. The need for training data is dramatically reduced by performing a two-step learning process based on lexical/phrase categorization.

The interest in using Finite-State Models in a large variety of applications is recently growing as more powerful techniques for learning them from examples have been developed. Language Understanding can be approached this way as a problem of language translation in which the target language is a formal language rather than a natural one. Finite-state transducers are used to model the translation process, and are automatically learned from training data consisting of pairs of natural-language/formal-language sentences. The need for training data is dramatically reduced by performing a two-level learning process based on lexical/phrase categorization. Successful experiments are presented on a task consisting in the "understanding" of Spanish natural-language sentences describing dates and times, where the target formal language is the one used in the popular Unix command "at".