Most dialogue architectures are either pipelined or, if agent-based, are restricted to a pipelined flow-of-information. The TRIPS dialogue architecture is agent-based and asynchronous, with several layers of information flow. We present this architecture and the synchronization issues we encountered in building a truly distributed, agentbased dialogue architecture.

We describe an implemented spoken-language dialogue system for a travel-planning domain, which accesses a commercially available travelinformation web-server and supports a flexible mixed-initiative dialogue strategy. We argue, based on data from initial Wizard-of-Oz experiments, that mixed-initiative strategies are appropriate for many types of user, but require more sophisticated architectures for processing of language and dialogue; we then use these observations to motivate an architecture which combines parallel deep and shallow natural language analysis engines and an agenda-driven dialogue manager. We outline the top-level processing strategy used by the dialogue manager, and also a novel formalism, which we call Flat Utterance Description, that allows us to reduce the output of the deep and shallow languageprocessing engines to a common representation.

With maturing speech technology, spoken dialogue systems are increasingly moving from research prototypes to fielded systems. The fielded systems however generally employ much simpler linguistic and dialogue processing strategies than the research prototypes. We describe an implemented spoken-language dialogue system for a travel planning domain which supports a mixed initiative dialogue strategy. The system accesses a commercially available travel information web-server. The system architecture combines both shallow and deep linguistic processors, partly so that a robust if shallow analysis is always available to the dialogue manager, and partly so that we can begin to examine where significant gains can be made by employing more advanced linguistic processing. We present the results of a preliminary investigation using data from a Wizard of Oz experiment. The results lend limited support to our original hypothesis that deep linguistic processing will prove useful at points where the ...

There is a growing interest in developing technologies for creating interactive dramas [13, 22]. Evaluating them, however, remains an open research problem. In this paper, we present a method for evaluating the technical and design approaches employed in a conversation-centered interactive drama. This method correlates players ’ subjective experience during conversational breakdowns, captured using retrospective protocols, with the corresponding AI processing in the input language understanding and dialog management subsystems. The methodology is employed to analyze conversation breakdowns in the interactive drama Façade. We find that the narrative cues offered by an interactive drama, coupled with believable character performance, can allow players to interpretively bridge system limitations and avoid experiencing a conversation breakdown. Further, we find that, contrary to standard practice for task-oriented conversation systems, using shallowly understood information as part of the system output hampers the player experience in an interactive drama.

This paper presents a novel technique that allows testing spoken dialogue systems by means of an automatic generation of conversations. The technique permits to easily test spoken dialogue systems under a variety of lab-simulated conditions, as it is easy to vary or change the utterance corpus used to check the performance of the system. The technique is based on the use of a module called user simulator whose purpose is to behave as real users when they interact with dialogue systems. The behaviour of the simulator is decided by means of diverse scenarios that represent the goals of the users. The simulator aim is to achieve the goals set in the scenarios during the interaction with the dialogue system. We have applied the technique to test a dialogue system developed in our lab. The test has been carried out considering different levels of white and babble noise as well as a VTS noise compensation technique. The results prove that the dialogue system performance is worse under the babble noise conditions. The VTS technique has been effective when dealing with noisy utterances and has lead to better experimental results, particularly for the white noise. The technique has permitted to detect problems in the dialogue strategies employed to handle confirmation turns and recognition errors, suggesting that these strategies must be improved. q 2002 Elsevier Science B.V. All rights reserved.

We present a collaborative problem-solving model for interaction between humans and automated systems.

This paper deals with improved confidence assessment for detecting recognition errors and out of dictionary names in a Spanish Recognizer of continuously spelled names over the telephone. We present a hypothesis-verification approach for spelled name recognition. We evaluate the system for several dictionaries, obtaining more than 90.0% recognition rate for a 10,000 name dictionary. For confidence scoring, we consider several features obtained from the different recognition stages. The paper investigates the ability of each feature set to detect recognition errors and names out of the spelling dictionary. We use a neural network to combine all the features in order to obtain the best confidence annotation. Using the data collected from 1,000 phone calls, it is shown that 57.9% incorrectly recognized names and 68.3% out of the spelling dictionary names are detected at a 5% false rejection rate. 1.

In the development of a speech understanding system, the recourse to stochastic techniques can greatly reduce the need for human expertise. A known disadvantage is that stochastic models require large annotated training corpora in order to reliably estimate model parameters. Manual semantic annotation of such corpora is tedious, expensive, and subject to inconsistencies. In order to decrease the development cost, this work investigates the performance of stochastic understanding models with two parameters: the use of automatically segmented data and the use of automatically learned lexical normalisation rules.

Connectionist classification and specific stochastic models in the understanding process of a dialogue system In this paper we present an approach to the application of specific models to the understanding process of a dialogue system. The previous classification process is done by means of Multilayer Perceptrons, and Hidden Markov Models are used for the semantic modeling. The task consists of answering telephone queries about train timetables, prices and services for long distance trains in Spanish. A comparison between a global understanding model and the specific models is presented. 1.

With maturing speech technology, spoken dialogue systems are increasingly moving from research prototypes to fielded systems. The fielded systems however generally employ much simpler linguistic and dialogue processing strategies than the research prototypes. We describe an implemented spoken-language dialogue system for a travel planning domain which supports a mixed initiative dialogue strategy. The system accesses a commercially available travel information web-server. The system architecture combines both shallow and deep linguistic processors, partly so that a robust if shallow analysis is always available to the dialogue manager, and partly so that we can begin to examine where significant gains can be made by employing more advanced linguistic processing. We present the results of a preliminary investigation using data from a Wizard of Oz experiment. The results lend limited support to our original hypothesis that deep linguistic processing will prove useful at points where the ...