To Whom are You Talking? A Deep Learning Model to Endow Social Robots with Addressee Estimation Skills

Communicating shapes our social word. For a robot to be considered social and being consequently integrated in our social environment it is fundamental to understand some of the dynamics that rule human-human communication. In this work, we tackle the problem of Addressee Estimation, the ability to understand an utterance's addressee, by interpreting and exploiting non-verbal bodily cues from the speaker. We do so by implementing an hybrid deep learning model composed of convolutional layers and LSTM cells taking as input images portraying the face of the speaker and 2D vectors of the speaker's body posture. Our implementation choices were guided by the aim to develop a model that could be deployed on social robots and be efficient in ecological scenarios. We demonstrate that our model is able to solve the Addressee Estimation problem in terms of addressee localisation in space, from a robot ego-centric point of view.Comment: Accepted version of a paper published at 2023 International Joint Conference on Neural Networks (IJCNN). Please find the published version and info to cite the paper at https://doi.org/10.1109/IJCNN54540.2023.10191452 . 10 pages, 8 Figures, 3 Table

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

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

Meetings and Meeting Modeling in Smart Environments

In this paper we survey our research on smart meeting rooms and its relevance for augmented reality meeting support and virtual reality generation of meetings in real time or off-line. The research reported here forms part of the European 5th and 6th framework programme projects multi-modal meeting manager (M4) and augmented multi-party interaction (AMI). Both projects aim at building a smart meeting environment that is able to collect multimodal captures of the activities and discussions in a meeting room, with the aim to use this information as input to tools that allow real-time support, browsing, retrieval and summarization of meetings. Our aim is to research (semantic) representations of what takes place during meetings in order to allow generation, e.g. in virtual reality, of meeting activities (discussions, presentations, voting, etc.). Being able to do so also allows us to look at tools that provide support during a meeting and at tools that allow those not able to be physically present during a meeting to take part in a virtual way. This may lead to situations where the differences between real meeting participants, human-controlled virtual participants and (semi-) autonomous virtual participants disappear

Towards Simulating Humans in Augmented Multi-party Interaction

Human-computer interaction requires modeling of the user. A user profile typically contains preferences, interests, characteristics, and interaction behavior. However, in its multimodal interaction with a smart environment the user displays characteristics that show how the user, not necessarily consciously, verbally and nonverbally provides the smart environment with useful input and feedback. Especially in ambient intelligence environments we encounter situations where the environment supports interaction between the environment, smart objects (e.g., mobile robots, smart furniture) and human participants in the environment. Therefore it is useful for the profile to contain a physical representation of the user obtained by multi-modal capturing techniques. We discuss the modeling and simulation of interacting participants in the European AMI research project

Twente Debate Corpus - A Multimodal Corpus for Head Movement Analysis

This paper introduces a multimodal discussion corpus for the study into head movement and turn-taking patterns in debates. Given that participants either acted alone or in a pair, cooperation and competition and their nonverbal correlates can be analyzed. In addition to the video and audio of the recordings, the corpus contains automatically estimated head movements, and manual annotations of who is speaking and who is looking where. The corpus consists of over 2 hours of debates, in 6 groups with 18 participants in total. We describe the recording setup and present initial analyses of the recorded data. We found that the person who acted as single debater speaks more and also receives more attention compared to the other debaters, also when corrected for the time speaking.We also found that a single debater was more likely to speak after a team debater. Future work will be aimed at further analysis of the relation between speaking and looking patterns, the outcome of the debate and perceived dominance of the debaters

Moving together: the organisation of non-verbal cues during multiparty conversation

PhDConversation is a collaborative activity. In face-to-face interactions interlocutors have mutual access to a shared space. This thesis aims to explore the shared space as a resource for coordinating conversation. As well demonstrated in studies of two-person conversations, interlocutors can coordinate their speech and non-verbal behaviour in ways that manage the unfolding conversation. However, when scaling up from two people to three people interacting, the coordination challenges that the interlocutors face increase. In particular speakers must manage multiple listeners. This thesis examines the use of interlocutors’ bodies in shared space to coordinate their multiparty dialogue. The approach exploits corpora of motion captured triadic interactions. The thesis first explores how interlocutors coordinate their speech and non-verbal behaviour. Inter-person relationships are examined and compared with artificially created triples who did not interact. Results demonstrate that interlocutors avoid speaking and gesturing over each other, but tend to nod together. Evidence is presented that the two recipients of an utterance have different patterns of head and hand movement, and that some of the regularities of movement are correlated with the task structure. The empirical section concludes by uncovering a class of coordination events, termed simultaneous engagement events, that are unique to multiparty dialogue. They are constructed using combinations of speaker head orientation and gesture orientation. The events coordinate multiple recipients of the dialogue and potentially arise as a result of the greater coordination challenges that interlocutors face. They are marked in requiring a mutually accessible shared space in order to be considered an effective interactional cue. The thesis provides quantitative evidence that interlocutors’ head and hand movements are organised by their dialogue state and the task responsibilities that the bear. It is argued that a shared interaction space becomes a more important interactional resource when conversations scale up to three people

A multimodal multiparty human-robot dialogue corpus for real world interaction

Kyoto University/Honda Research Institute Japan Co.,Ltd.LREC 2018 Special Speech Sessions "Speech Resources Collection in Real-World Situations"; Phoenix Seagaia Conference Center, Miyazaki; 2018-05-09We have developed the MPR multimodal dialogue corpus and describe research activities using the corpus aimed for enabling multiparty human-robot verbal communication in real-world settings. While aiming for that as the final goal, the immediate focus of our project and the corpus is non-verbal communication, especially social signal processing by machines as the foundation of human-machine verbal communication. The MPR corpus stores annotated audio-visual recordings of dialogues between one robot and one or multiple (up to tree) participants. The annotations include speech segment, addressee of speech, transcript, interaction state, and, dialogue act types. Our research on multiparty dialogue management, boredom recognition, response obligation recognition, surprise detection and repair detection using the corpus is briefly introduced, and an analysis on repair in multiuser situations is presented. It exhibits richer repair behaviors and demands more sophisticated repair handling by machines