Technologies that allow autonomous robots and computer systems to quickly recognize and interact with individuals in a group setting has the potential to enable a wide range of personalized experiences. However, existing solutions fail to both identify and locate individuals with enough speed to enable seamless interactions in very dynamic environments that require fast, implicit, non-intrusive, and ubiquitous recognition of users. In this work, we present a hybrid computer vision and RFID system that uses a novel reverse synthetic aperture technique to recover the relative motion paths of an RFID tags worn by people and correlate that to physical motion paths of individuals as measured with a 3D depth camera. Results show that our real-time system is capable of simultaneously recognizing and correctly assigning IDs to individuals within 4 seconds with 96.6% accuracy and groups of five people in 7 seconds with 95% accuracy. In order to test the effectiveness of this approach in realistic scenarios, groups of five participants play an interactive quiz game with an autonomous robot, resulting in an ID assignment accuracy of 93.3%.

Effective teleoperation requires real-time control of a remote robotic system. In this work, we develop a controller for realizing smooth and accurate motion of a robotic head with application to a teleoperation system for the Furhat robot head [1], which we call TeleFurhat. The controller uses the head motion of an operator measured by a Microsoft Kinect 2 sensor as reference and applies a processing framework to condition and render the motion on the robot head. The processing framework includes a pre-filter based on a moving average filter, a neural network-based model for improving the accuracy of the raw pose measurements of Kinect, and a constrained-state Kalman filter that uses a minimum jerk model to smooth motion trajectories and limit the magnitude of changes in position, velocity, and acceleration. Our results demonstrate that the robot can reproduce the human head motion in real time with a latency of approximately 100 to 170 ms while operating within its physical limits. Furthermore, viewers prefer our new method over rendering the raw pose data from Kinect.

A system's ability to understand and model a human's engagement during an interactive task is important for both adapting its behavior to the moment and achieving a coherent interaction over time. Standard practice for creating such a capability requires uncovering and modeling the multimodal cues that predict engagement in a given task environment. The first step in this methodology is to have human coders produce "gold standard" judgments of sample behavior. In this paper we report results from applying this first step to the complex and varied behavior of children playing a fast-paced, speech-controlled, side-scrolling game called Mole Madness. We introduce a concrete metric for engagement-willingness to continue the interaction--that leads to better inter-coder judgments for children playing in pairs, explore how coders perceive the relative contribution of audio and visual cues, and describe engagement trends and patterns in our population. We also examine how the measures change when the same children play Mole Madness with a robot instead of a peer. We conclude by discussing the implications of the differences within and across play conditions for the automatic estimation of engagement and the extension of our autonomous robot player into a "buddy" that can individualize interaction for each player and game.

In this paper, we describe a project that explores a novel experimental setup towards building a spoken, multi-modally rich, and human-like multiparty tutoring robot. A human-robot interaction setup is designed, and a human-human dialogue corpus is collected. The corpus targets the development of a dialogue system platform to study verbal and nonverbal tutoring strategies in multiparty spoken interactions with robots which are capable of spoken dialogue. The dialogue task is centered on two participants involved in a dialogue aiming to solve a card-ordering game. Along with the participants sits a tutor (robot) that helps the participants perform the task, and organizes and balances their interaction. Different multimodal signals captured and auto-synchronized by different audio-visual capture technologies, such as a microphone array, Kinects, and video cameras, were coupled with manual annotations. These are used build a situated model of the interaction based on the participants personalities, their state of attention, their conversational engagement and verbal dominance, and how that is correlated with the verbal and visual feed-back, turn-management, and conversation regulatory actions generated by the tutor. Driven by the analysis of the corpus, we will show also the detailed design methodologies for an affective, and multimodally rich dialogue system that allows the robot to measure incrementally the attention states, and the dominance for each participant, allowing the robot head Furhat to maintain a well-coordinated, balanced, and engaging conversation, that attempts to maximize the agreement and the contribution to solve the task.

Furhat [1] is a robot head that deploys a back-projected animated face that is realistic and human-like in anatomy. Furhat relies on a state-of-the-art facial animation architecture allowing accurate synchronized lip movements with speech, and the control and generation of non-verbal gestures, eye movements and facial expressions.

In this paper, we present a brief summary of the international workshop on Modeling Multiparty, Multimodal Interactions. The UM3I 2014 workshop is held in conjunction with the ICMI 2014 conference. The workshop will highlight recent developments and adopted methodologies in the analysis and modeling of multiparty and multimodal interactions, the design and implementation principles of related human-machine interfaces, as well as the identification of potential limitations and ways of overcoming them.

In order to understand and model the dynamics between interaction phenomena such as gaze and speech in face-to-face multiparty interaction between humans, we need large quantities of reliable, objective data of such interactions. To date, this type of data is in short supply. We present a data collection setup using automated, objective techniques in which we capture the gaze and speech patterns of triads deeply engaged in a high-stakes quiz game. The resulting corpus consists of five one-hour recordings, and is unique in that it makes use of three state-of-the-art gaze trackers (one per subject) in combination with a state-of-theart conical microphone array designed to capture roundtable meetings. Several video channels are also included. In this paper we present the obstacles we encountered and the possibilities afforded by a synchronised, reliable combination of large-scale multi-party speech and gaze data, and an overview of the first analyses of the data. Index Terms: multimodal corpus, multiparty dialogue, gaze patterns, multiparty gaze.

We will show in this demonstrator an advanced multimodal and multiparty spoken conversational system using Furhat, a robot head based on projected facial animation. Furhat is a human-like interface that utilizes facial animation for physical robot heads using back-projection. In the system, multimodality is enabled using speech and rich visual input signals such as multi-person real-time face tracking and microphone tracking. The demonstrator will showcase a system that is able to carry out social dialogue with multiple interlocutors simultaneously with rich output signals such as eye and head coordination, lips synchronized speech synthesis, and non-verbal facial gestures used to regulate fluent and expressive multiparty conversations.

In this paper, we present IrisTK - a toolkit for rapid development of real-time systems for multi-party face-to-face interaction. The toolkit consists of a message passing system, a set of modules for multi-modal input and output, and a dialog authoring language based on the notion of statecharts. The toolkit has been applied to a large scale study in a public museum setting, where the back-projected robot head Furhat interacted with the visitors in multi-party dialog.

The perception of gaze plays a crucial role in human-human interaction. Gaze has been shown to matter for a number of aspects of communication and dialogue, especially for managing the flow of the dialogue and participant attention, for deictic referencing, and for the communication of attitude. When developing embodied conversational agents (ECAs) and talking heads, modeling and delivering accurate gaze targets is crucial. Traditionally, systems communicating through talking heads have been displayed to the human conversant using 2D displays, such as flat monitors. This approach introduces severe limitations for an accurate communication of gaze since 2D displays are associated with several powerful effects and illusions, most importantly the Mona Lisa gaze effect, where the gaze of the projected head appears to follow the observer regardless of viewing angle. We describe the Mona Lisa gaze effect and its consequences in the interaction loop, and propose a new approach for displaying talking heads using a 3D projection surface (a physical model of a human head) as an alternative to the traditional flat surface projection. We investigate and compare the accuracy of the perception of gaze direction and the Mona Lisa gaze effect in 2D and 3D projection surfaces in a five subject gaze perception experiment. The experiment confirms that a 3D projection surface completely eliminates the Mona Lisa gaze effect and delivers very accurate gaze direction that is independent of the observer's viewing angle. Based on the data collected in this experiment, we rephrase the formulation of the Mona Lisa gaze effect. The data, when reinterpreted, confirms the predictions of the new model for both 2D and 3D projection surfaces. Finally, we discuss the requirements on different spatially interactive systems in terms of gaze direction, and propose new applications and experiments for interaction in a human-ECA and a human-robot settings made possible by this technology.

* names in alphabetical order This paper presents an Acoustic-to-Articulatory inversion method based on local regression. Two types of local regression, a non-parametric and a local linear regression have been applied on a corpus containing simultaneous recordings of positions of articulators and the corresponding acoustics. A maximum likelihood trajectory smoothing using the estimated dynamics of the articulators is also applied on the regression estimates. The average root mean square error in estimating articulatory positions, given the acoustics, is 1.56 mm for the nonparametric regression and 1.52 mm for the local linear regression. The local linear regression is found to perform significantly better than regression using Gaussian Mixture Models using the same acoustic and articulatory features.

This paper presents an approach to estimating word level prominence in Swedish using syllable level features. The paper discusses the mismatch problem of annotations between word level perceptual prominence and its acoustic correlates, context, and data scarcity. 200 sentences are annotated by 4 speech experts with prominence on 3 levels. A linear model for feature extraction is proposed on a syllable level features, and weights of these features are optimized to match word level annotations. We show that using syllable level features and estimating weights for the acoustic correlates to minimize the word level estimation error gives better detection accuracy compared to word level features, and that both features exceed the baseline accuracy. Index Terms: prominence, accent, focus, syllable emphasis 1.

In this paper we present recent results on the development of the SynFace lip synchronized talking head towards multilinguality, varying signal conditions and noise robustness in the Hearing at Home project. We then describe the large scale hearing impaired user studies carried out for three languages. The user tests focus on measuring the gain in Speech Reception Threshold in Noise when using SynFace, and on measuring the effort scaling when using SynFace by hearing impaired people. Preliminary analysis of the results does not show significant gain in SRT or in effort scaling. But looking at inter-subject variability, it is clear that many subjects benefit from SynFace especially with speech with stereo babble noise.

The Hearing at Home (HaH) project focuses on the needs of hearing-impaired people in home environments. The project is researching and developing an innovative media-center solution for hearing support, with several integrated features that support perception of speech and audio, such as individual loudness amplification, noise reduction, audio classification and event detection, and the possibility to display an animated talking head providing real-time speechreading support. In this paper we provide a brief project overview and then describe some recent results related to the audio classifier and the talking head. As the talking head expects clean speech input, an audio classifier has been developed for the task of classifying audio signals as clean speech, speech in noise or other. The mean accuracy of the classifier was 82%. The talking head (based on technology from the SynFace project) has been adapted for German, and a small speech-in-noise intelligibility experiment was conducted where sentence recognition rates increased from 3 % to 17 % when the talking head was present. Index Terms: Sound classification, speech processing, hearing impairment, communication support, talking heads, speechreading 1.

Lip synchronization is the process of generating natural lip movements from a speech signal. In this work we address the lip-sync problem using an automatic phone recognizer that generates a phone lattice carrying posterior probabilities. The acoustic feature vector contains the posterior probabilities of all the phones over a time window centered at the current time point. Hence this representation characterizes the phone recognition output including the confusion patterns caused by its limited accuracy. A 3D face model with varying texture is computed by analyzing a video recording of the speaker using a 3D morphable model. Training a neural network using 30 000 data vectors from an audiovisual recording in Dutch resulted in a very good simulation of the face on independent data sets of the same or of a different speaker.