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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.

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This paper describes a technique for enabling a speech understanding system to deal with sentences for which some monosyllabic words are not recognized. Such words are supposed to act as mere syntactic markers within the system linguistic domain. This result is achieved by combining a modified caseframe approach to linguistic knowledge representation with a parsing strategy able to integrate expectations from the language model and predictions from words. Experimental results show that the proposed technique permits to greatly increase the quota of corrupted sentences correctly understandable without sensibly decreasing parsing efficiency.