This paper presents Dialogos, a real-time system for human-machine spoken dialogue on the telephone in task-oriented domains. The system has been tested in a large trial with inexperienced users and it has proved robust enough to allow spontaneous interactions both to users which get good recognition performance and to the ones which get lower scores. The robust behavior of the system has been achieved by combining the use of specific language models during the recognition phase of analysis, the tolerance toward spontaneous speech phenomena, the activity of a robust parser, and the use of pragmatic-based dialogue knowledge. This integration of the different modules allows to deal with partial or total breakdowns of the different levels of analysis. We report the field trial data of the system and the evaluation results of the overall system and of the submodules.

This paper describes a Continuous Speech Understanding System that allows information services to be accessed through the telephone line. It accepts queries within a restricted semantic domain, expressed in free but syntactically correct natural language, with a lexicon of the order of 800 words. In the implementation here described, a user can access an electronic mailbox or a train information service through a PABX telephone line. The architecture of the system is based on two main modules that represent and use different knowledge sources. A speaker independent recognition module generates, for each utterance, a lattice of word hypotheses which is the interface to an understanding module that performs the syntactic and semantic analysis. The recognition module is based on Hidden Markov Models of subword units, and performs the acoustic decoding process according to a beam search strategy. The understanding module finds the most likely sequence of words and represents its meaning in a format which facilitates the access to a database. It makes use of a modified caseframe analysis guided by the word hypotheses scores. Experiments were performed with 600 sentences from 10 speakers on the E-Mail application task. Using 15 Gaussian mixtures per state, a word accuracy of 75.7 was obtained with a test vocabulary of 787 words and no linguistic constraints. Linguistic processing of the corresponding lattices achieved a sentence understanding rate of 82%.

A continuous speech recognition and understanding system is presented that accepts queries about a restricted geographical domain, expressed in free but syntactically correct natural language, with a lexicon of the order of one thousand words. A lattice of word candidates hypothesized by the speaker dependent recognition level is the interface to an understanding module that performs the syntactic and semantic analysis. The recognition subsystem generates word hypotheses by exploiting hidden Markov models of sub-word units. Bottom-up constraints are also introduced to restrict the set of candidate words. The understanding module determines the most likely sequence of words and represents its meaning in a parse-tree suitable to access a database. It makes use of a modified caseframe analysis driven by the word hypotheses likelihood scores. The results of a set of experiments performed in 150 sentences collected from one speaker are given.>