User modeling for spoken dialogue system evaluation

Eckert, Wieland; Levin, Esther; Pieraccini, Roberto

doi:10.1109/asru.1997.658991

Cited by 127 publications

(110 citation statements)

References 8 publications

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“…Simulations can be run for different groups of users, as was previously done by Eckert et al (1997). These authors define four very distinct user groups with extreme behavior patterns (e.g.…”

Section: User Group Editormentioning

confidence: 99%

“…While this could be done with real user tests, they claim that cheaper, quicker, and more objective methods are needed especially during the early development states. In the same year, Eckert et al (1997) proposed a statistical approach to model the interaction between users and a spoken dialog system. In their model, user and system actions are modeled on the intentional level, and user actions are dependent on the previous system action and the current user state.…”

Section: User Simulation For Spoken Dialog Systemsmentioning

confidence: 99%

See 1 more Smart Citation

Analysis of a new simulation approach to dialog system evaluation

Engelbrecht

Quade

Möller

2009

Speech Communication

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Section: User Group Editormentioning

confidence: 99%

Section: User Simulation For Spoken Dialog Systemsmentioning

confidence: 99%

Analysis of a new simulation approach to dialog system evaluation

Engelbrecht

Quade

Möller

2009

Speech Communication

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“…In [48], Eckert, Levin and Pieraccini introduced the use of statistical models to predict the next user action by means of a n-gram model. The proposed model has the advantage of being both statistical and task-independent.…”

Section: Modeling the User Intentionmentioning

confidence: 99%

Modeling the user state for context-aware spoken interaction in ambient assisted living

2014

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Ambient Assisted Living (AAL) systems must provide adapted services easily accessible by a wide variety of users. This can only be possible if the communication between the user and the system is carried out through an interface that is simple, rapid, effective, and robust. Natural language interfaces such as dialog systems fulfil these requisites, as they are based on a spoken conversation that resembles human communication. In this paper, we enhance systems interacting in AAL domains by means of incorporating context-aware conversational agents that consider the external context of the interaction and predict the user's state. The user's state is built on the basis of their emotional state and intention, and it is recognized by means of a module conceived as an intermediate phase between natural language understanding and dialog management in the architecture of the conversational agent. This prediction, carried out for each user turn in the dialog, makes it possible to adapt the system dynamically to the user's needs. We have evaluated our proposal developing a context-aware system adapted to patients suffering from chronic pulmonary diseases, and provide a detailed discussion of the positive influenc of our proposal in the success of the interaction, the information and services provided, as well as the perceived quality.

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“…In (Eckert et al, 1997(Eckert et al, , 1998, Eckert, Levin and Pieraccini introduced the use of statistical models to predict the next user action by means of a ngram model. The proposed model has the advantage of being both statistical and task-independent.…”

Section: Related Work Evaluation Of Dialog Systemsmentioning

confidence: 99%

“…Two different methodologies are described to select the next user action given a history of information states. The first method uses n-grams (Eckert et al, 1997), but with values of n from 2 to 5 to consider a longer history of the dialog. The best results are obtained with 4-grams.…”

Section: Related Work Evaluation Of Dialog Systemsmentioning

confidence: 99%