Exploiting emotions to disambiguate dialogue acts

Bosma, W.E.; André, Elisabeth

doi:10.1145/964442.964459

Cited by 43 publications

(12 citation statements)

References 20 publications

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“…Bosma and André, 2004;Jokinen et al, 2001;Samuel et al, 1998) since we built our sentence opener interface. Machine learning classifiers can be used to automatically label an utterance with the corresponding dialogue act.…”

Section: Future Directionsmentioning

confidence: 99%

Using Dialogue Features to Predict Trouble During Collaborative Learning

Goodman

Linton

Gaimari

et al. 2005

User Model User-Adap Inter

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A web-based, collaborative distance-learning system that will allow groups of students to interact with each other remotely and with an intelligent electronic agent that will aid them in their learning has the potential for improving on-line learning. The agent would follow the discussion and interact with the participants when it detects learning trouble of some sort, such as confusion about the problem they are working on or a participant who is dominating the discussion or not interacting with the other participants. In order to recognize problems in the dialogue, we investigated conversational elements that can be utilized as predictors for effective and ineffective interaction between human students. These elements can serve as the basis for student and group models. In this paper, we discuss group interaction during collaborative learning, our representation of participant dialogue, and the statistical models we are using to determine the role being played by a participant at any point in the dialogue and the effectiveness of the group. We also describe student and group models that can be built using conversational elements and discuss one set that we built to illustrate their potential value in collaborative learning.

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Section: Future Directionsmentioning

confidence: 99%

Using Dialogue Features to Predict Trouble During Collaborative Learning

Goodman

Linton

Gaimari

et al. 2005

User Model User-Adap Inter

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“…Bosma et al [2] proposed a method to use the user's emotional state in order to disambiguate dialogue acts. They restrict themselves to pedagogical agents that offer a text-based natural-language interface to assist the user in communicating by typing.…”

Section: Related Workmentioning

confidence: 99%

A method to dynamically estimate emphasizing points and degree by using verbal and nonverbal information and physiological indices

Ohmoto

Kataoka

Miyake

et al. 2011

2011 IEEE International Conference on Granular Computing

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The differences in the concepts of values which are the basis of human actions are important when we analyze intercultural communication. The purpose of this study is to estimate emphasizing points for decision-making, which is one of the concepts of values. For this purpose, we experimentally investigate how we could estimate discoveries new factors and increase degree of emphasis by using verbal information, nonverbal behavior and physiological indices. From the results of the investigation, we propose a method to dynamically estimate emphasizing points when two propositions were explained and a participant was asked what were his/her demands. We also conducted an experiment to evaluate our proposed method. As a result, we confirmed that the proposed method could accurately estimate the emphasizing points.

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“…In current research several approaches to classify and to recognise human emotions exist. These range from gestures and facial expressions interpretation in multimodal systems to physiological measurements (Bosma and André 2004) or linguistic analysis. We concentrate on the recognition of emotions from the speech signal.…”

Section: Adaptivenessmentioning

confidence: 99%

Challenges in speech-based human–computer interfaces

Minker¹,

Pittermann²,

Pittermann³

et al. 2007

Int J Speech Technol

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In this article we present an overview of our current research activities falling into the scope of developing advanced spoken language dialogue systems. These systems need to react flexibly and adaptively depending on the current status of the user and the situation of use. In particular, they require emotion recognition and adaptive dialogue management techniques. Advanced dialogue systems also need proactive capabilities to act as intelligent assistants to their users.

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Exploiting emotions to disambiguate dialogue acts

Cited by 43 publications

References 20 publications

Using Dialogue Features to Predict Trouble During Collaborative Learning

Using Dialogue Features to Predict Trouble During Collaborative Learning

A method to dynamically estimate emphasizing points and degree by using verbal and nonverbal information and physiological indices

Challenges in speech-based human–computer interfaces

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