Are you being addressed?: real-time addressee detection to support remote participants in hybrid meetings

A meeting assistant agent for (remote) participants in hybrid meetings has been developed. Its task is to monitor the meeting conversation and notify the user when he is being addressed. This paper presents the experiments that have been performed to develop machine classifiers to decide if “You are being addressed��? where “You��? refers to a fixed (remote) participant in a meeting. The experimental results back up the choices made regarding the selection of data, features, and classification methods. We discuss variations of the addressee classification problem that have been considered in the literature and how suitable they are for addressing detection in a system that plays a role in a live meeting

How Do I Address You? Modelling addressing behavior based on an analysis of a multi-modal corpora of conversational discourse

Addressing is a special kind of referring and thus principles of multi-modal referring expression generation will also be basic for generation of address terms and addressing gestures for conversational agents. Addressing is a special kind of referring because of the different (second person instead of object) role that the referent has in the interaction. Based on an analysis of addressing behaviour in multi-party face-to-face conversations (meetings, TV discussions as well as theater plays), we present outlines of a model for generating multi-modal verbal and non-verbal addressing behaviour for agents in multi-party interactions

Exploiting `Subjective' Annotations

Many interesting phenomena in conversation can only be annotated as a subjective task, requiring interpretative judgements from annotators. This leads to data which is annotated with lower levels of agreement not only due to errors in the annotation, but also due to the differences in how annotators interpret conversations. This paper constitutes an attempt to find out how subjective annotations with a low level of agreement can profitably be used for machine learning purposes. We analyse the (dis)agreements between annotators for two different cases in a multimodal annotated corpus and explicitly relate the results to the way machine-learning algorithms perform on the annotated data. Finally we present two new concepts, namely `subjective entity' classifiers resp. `consensus objective' classifiers, and give recommendations for using subjective data in machine-learning applications.\u

A comparison of addressee detection methods for multiparty conversations

Several algorithms have recently been proposed for recognizing addressees in a group conversational setting. These algorithms can rely on a variety of factors including previous conversational roles, gaze and type of dialogue act. Both statistical supervised machine learning algorithms as well as rule based methods have been developed. In this paper, we compare several algorithms developed for several different genres of muliparty dialogue, and propose a new synthesis algorithm that matches the performance of machine learning algorithms while maintaning the transparancy of semantically meaningfull rule-based algorithms

Feedback presentation for mobile personalised digital physical activity coaching platforms

User interface design and feedback are important in personalised behavior change support systems. This paper discusses two service platforms that monitor user’s physical activity through wearable sensors and that present the user personalised feedback. Important principles for effectiveness of such systems are personalisation or tailoring, context- awareness, feedback and interaction. We focus here on the presentation of feedback to the user. We present results of a number of short and long term user studies in which we compare different forms of feedback presentation: text, graphics and with or without an anthropomorphic graphical talking character. Results show that although some users like the talking character they don’t have a positive effect on adherence to the activity program. The outcomes of the user evaluations support our beliefs that personal motivation is of primary importance for the effectiveness of these systems. Technical challenges ahead are to support more personal and context-aware feedback, more variations as well as the possibility for more interaction with the coaching system

Weakly Restricted Stochastic Grammars

A new type of stochastic grammars is introduced for investigation: weakly restricted stochastic grammars. In this paper we will concentrate on the consistency problem. To find conditions for stochastic grammars to be consistent, the theory of multitype Galton-Watson branching processes and generating functions is of central importance.\ud The unrestricted stochastic grammar formalism generates the same class of languages as the weakly restricted formalism. The inside-outside algorithm is adapted for use with weakly restricted grammars