Towards responsive Sensitive Artificial Listeners

This paper describes work in the recently started project SEMAINE, which aims to build a set of Sensitive Artificial Listeners – conversational agents designed to sustain an interaction with a human user despite limited verbal skills, through robust recognition and generation of non-verbal behaviour in real-time, both when the agent is speaking and listening. We report on data collection and on the design of a system architecture in view of real-time responsiveness

Who's afraid of job interviews? Definitely a question for user modelling

We define job interviews as a domain of interaction that can be modelled automatically in a serious game for job interview skills training. We present four types of studies: (1) field-based human-to-human job interviews, (2) field-based computer-mediated human-to-human interviews, (3) lab-based wizard of oz studies, (4) field-based human-to agent studies. Together, these highlight pertinent questions for the user modelling eld as it expands its scope to applications for social inclusion. The results of the studies show that the interviewees suppress their emotional behaviours and although our system recognises automatically a subset of those behaviours, the modelling of complex mental states in real-world contexts poses a challenge for the state-of-the-art user modelling technologies. This calls for the need to re-examine both the approach to the implementation of the models and/or of their usage for the target contexts

Affective games:a multimodal classification system

Affective gaming is a relatively new field of research that exploits human emotions to influence gameplay for an enhanced player experience. Changes in player’s psychology reflect on their behaviour and physiology, hence recognition of such variation is a core element in affective games. Complementary sources of affect offer more reliable recognition, especially in contexts where one modality is partial or unavailable. As a multimodal recognition system, affect-aware games are subject to the practical difficulties met by traditional trained classifiers. In addition, inherited game-related challenges in terms of data collection and performance arise while attempting to sustain an acceptable level of immersion. Most existing scenarios employ sensors that offer limited freedom of movement resulting in less realistic experiences. Recent advances now offer technology that allows players to communicate more freely and naturally with the game, and furthermore, control it without the use of input devices. However, the affective game industry is still in its infancy and definitely needs to catch up with the current life-like level of adaptation provided by graphics and animation

Yeah, Right, Uh-Huh: A Deep Learning Backchannel Predictor

Using supporting backchannel (BC) cues can make human-computer interaction more social. BCs provide a feedback from the listener to the speaker indicating to the speaker that he is still listened to. BCs can be expressed in different ways, depending on the modality of the interaction, for example as gestures or acoustic cues. In this work, we only considered acoustic cues. We are proposing an approach towards detecting BC opportunities based on acoustic input features like power and pitch. While other works in the field rely on the use of a hand-written rule set or specialized features, we made use of artificial neural networks. They are capable of deriving higher order features from input features themselves. In our setup, we first used a fully connected feed-forward network to establish an updated baseline in comparison to our previously proposed setup. We also extended this setup by the use of Long Short-Term Memory (LSTM) networks which have shown to outperform feed-forward based setups on various tasks. Our best system achieved an F1-Score of 0.37 using power and pitch features. Adding linguistic information using word2vec, the score increased to 0.39

String-based audiovisual fusion of behavioural events for the assessment of dimensional affect

The automatic assessment of affect is mostly based on feature-level approaches, such as distances between facial points or prosodic and spectral information when it comes to audiovisual analysis. However, it is known and intuitive that behavioural events such as smiles, head shakes or laughter and sighs also bear highly relevant information regarding a subject's affective display. Accordingly, we propose a novel string-based prediction approach to fuse such events and to predict human affect in a continuous dimensional space. Extensive analysis and evaluation has been conducted using the newly released SEMAINE database of human-to-agent communication. For a thorough understanding of the obtained results, we provide additional benchmarks by more conventional feature-level modelling, and compare these and the string-based approach to fusion of signal-based features and string-based events. Our experimental results show that the proposed string-based approach is the best performing approach for automatic prediction of Valence and Expectation dimensions, and improves prediction performance for the other dimensions when combined with at least acoustic signal-based features