A Listening Agent Exhibiting Variable Behaviour

Bevacqua, Elisabetta; Mancini, Maurizio; Pélachaud, Catherine

doi:10.1007/978-3-540-85483-8_27

Cited by 45 publications

(30 citation statements)

References 15 publications

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“…Other inspiring lines of research relate to social interaction (Bevacqua et al, 2008;Saerbeck et al, 2010;Kopp, 2010) and rapport in dialog. Rapport does not necessarily involve deep or lasting feelings between the interlocutors, but includes the sort of positive first impression that may arise from a one-off interaction (Grahe and Bernieri, 1999;Gratch et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

Achieving rapport with turn-by-turn, user-responsive emotional coloring

Acosta

Ward

2011

Speech Communication

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People in dialog use a rich set of nonverbal behaviors, including variations in the prosody of their utterances. Such behaviors, often emotion-related, call for appropriate responses, but today's spoken dialog systems lack the ability to do this. Recent work has shown how to recognize user emotions from prosody and how to express system-side emotions with prosody, but demonstrations of how to combine these functions to improve the user experience have been lacking. Working with a corpus of conversations with students about graduate school, we analyzed the emotional states of the interlocutors, utterance by utterance, using three dimensions: activation, evaluation, and power. We found that the emotional coloring of the speaker's utterance could be largely predicted from the emotion shown by her interlocutor in the immediately previous utterance. This finding enabled us to build Gracie, the first spoken dialog system that recognizes a user's emotional state from his or her speech and gives a response with appropriate emotional coloring. Evaluation with 36 subjects showed that they felt significantly more rapport with Gracie than with either of two controls. This shows that dialog systems can tap into this important level of interpersonal interaction using today's technology.

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Section: Introductionmentioning

confidence: 99%

Achieving rapport with turn-by-turn, user-responsive emotional coloring

Acosta

Ward

2011

Speech Communication

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“…In this role, the agent may provide various types of non-verbal feedback to the user (see for example [7]). In conjunction with speaking role capabilities currently being investigated, this would allow for the construction of more sophisticated scenarios.…”

Section: Discussionmentioning

confidence: 99%

“…In work concerning eye-gaze for interaction with conversational agents, some approaches have cast the ECA in the primarily role of a listener, for example, as a SAL, or sensitive artificial listener [7], providing feedback to a user. Attentive presentation agents [8] use eye gaze to alter the ongoing behaviour of embodied agents in real-time.…”

Section: Introductionmentioning

confidence: 99%

Modelling user attention for human-agent interaction

Peters

Asteriadis

Rebolledo-Méndez³

2009

2009 10th Workshop on Image Analysis for Multimedia Interactive Services

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In this work, we propose a design for a user attention model featuring three core components. Our system components can work in real-time, offering indications of user attention from different sensory inputs (both visual and neurophysiological). Intention of the current work is to keep the equipment as unintrusive as possible, while keeping the confidence of the inputs as high as possible. We discuss potential applications of such a system, particularly with respect to evaluating user attentive behaviour during human-agent interactions and as a more natural interface for interacting with agents.

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“…Sera Project [116]), or the possibility of including additional capabilities such as memory, cognition, or learning (e.g., Companions [35], Classic [164], LIREC [83], Soprano [145], Humaine [6], Callas [16], Cogniron [85], Semaine [18], or MUDIS [125]). …”

Section: Related Workmentioning

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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A Listening Agent Exhibiting Variable Behaviour

Cited by 45 publications

References 15 publications

Achieving rapport with turn-by-turn, user-responsive emotional coloring

Achieving rapport with turn-by-turn, user-responsive emotional coloring

Modelling user attention for human-agent interaction

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

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