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

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 fulfill 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 influence of our proposal in the success of the interaction, the information and services provided, as well as the perceived quality.This work was supported in part by Projects MINECO TEC2012-37832-C02-01, CICYT TEC2011-28626-C02- 02, CAM CONTEXTS (S2009/TIC-1485

Building multi-domain conversational systems from single domain resources

Current Advances In The Development Of Mobile And Smart Devices Have Generated A Growing Demand For Natural Human-Machine Interaction And Favored The Intelligent Assistant Metaphor, In Which A Single Interface Gives Access To A Wide Range Of Functionalities And Services. Conversational Systems Constitute An Important Enabling Technology In This Paradigm. However, They Are Usually Defined To Interact In Semantic-Restricted Domains In Which Users Are Offered A Limited Number Of Options And Functionalities. The Design Of Multi-Domain Systems Implies That A Single Conversational System Is Able To Assist The User In A Variety Of Tasks. In This Paper We Propose An Architecture For The Development Of Multi-Domain Conversational Systems That Allows: (1) Integrating Available Multi And Single Domain Speech Recognition And Understanding Modules, (2) Combining Available System In The Different Domains Implied So That It Is Not Necessary To Generate New Expensive Resources For The Multi-Domain System, (3) Achieving Better Domain Recognition Rates To Select The Appropriate Interaction Management Strategies. We Have Evaluated Our Proposal Combining Three Systems In Different Domains To Show That The Proposed Architecture Can Satisfactory Deal With Multi-Domain Dialogs. (C) 2017 Elsevier B.V. All Rights Reserved.Work partially supported by projects MINECO TEC2012-37832-C02-01, CICYT TEC2011-28626-C02-02

Measuring the differences between human-human and human-machine dialogs

In this paper, we assess the applicability of user simulation techniques to generate dialogs which are similar to real human-machine spoken interactions.To do so, we present the results of the comparison between three corpora acquired by means of different techniques. The first corpus was acquired with real users.A statistical user simulation technique has been applied to the same task to acquire the second corpus. In this technique, the next user answer is selected by means of a classification process that takes into account the previous dialog history, the lexical information in the clause, and the subtask of the dialog to which it contributes. Finally, a dialog simulation technique has been developed for the acquisition of the third corpus. This technique uses a random selection of the user and system turns, defining stop conditions for automatically deciding if the simulated dialog is successful or not. We use several evaluation measures proposed in previous research to compare between our three acquired corpora, and then discuss the similarities and differences with regard to these measures

A proposal for the development of adaptive spoken interfaces to access the Web

Spoken dialog systems have been proposed as a solution to facilitate a more natural human–machine interaction. In this paper, we propose a framework to model the user׳s intention during the dialog and adapt the dialog model dynamically to the user needs and preferences, thus developing more efficient, adapted, and usable spoken dialog systems. Our framework employs statistical models based on neural networks that take into account the history of the dialog up to the current dialog state in order to predict the user׳s intention and the next system response. We describe our proposal and detail its application in the Let׳s Go spoken dialog system.Work partially supported by Projects MINECO TEC2012-37832- C02-01, CICYT TEC2011-28626-C02-02, CAM CONTEXTS (S2009/ TIC-1485