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

Abstract

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.

In the hearts and minds of employees: A model of pre-adoptive appraisal toward artificial intelligence in organizations

“…There is an inherent complexity in simulating human conversations as people have diverse ways of communicating (Griol, Molina, & Callejas, 2014). Natural language understanding (NLU) occurs when the program aims at simulating the human being's understanding capabilities: the programs developed can then claim some cognitive validity (Sabah, 2010).…”

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“…In traditional conversational systems, the dialog manager receives a semantic representation of the user's previous input, and uses dialog history and different user or interaction models and/or external information sources to decide on the next system response [115]. Affective dialog management implies processing affective information as another input to the dialog manager or the user model used by the manager.…”

“…Besides, engagement policies that are coupled with user models [79,145] are required to vary their behavior in a more subtle way. Indeed, a sustained interaction with an agent that always behaves in the same way would drastically decrease user satisfaction over time [115], but they are also useful in short-term relations to maintain the interaction with the user [146].…”

Lightweight Adaptation of Classifiers to Users and Contexts: Trends of the Emerging Domain

“…Probabilities of emotional categories, associated with these events, were combined with probabilities estimated from acoustic and prosodic features in two stages, employing each a nontrainable fusion, for example, the voting, average, or product of the probabilities. Similarly, dialog acts served as context for differentiating between “doubtful” and “bored” user states in [ 111 ].…”