Robust Modeling of Epistemic Mental States

This work identifies and advances some research challenges in the analysis of facial features and their temporal dynamics with epistemic mental states in dyadic conversations. Epistemic states are: Agreement, Concentration, Thoughtful, Certain, and Interest. In this paper, we perform a number of statistical analyses and simulations to identify the relationship between facial features and epistemic states. Non-linear relations are found to be more prevalent, while temporal features derived from original facial features have demonstrated a strong correlation with intensity changes. Then, we propose a novel prediction framework that takes facial features and their nonlinear relation scores as input and predict different epistemic states in videos. The prediction of epistemic states is boosted when the classification of emotion changing regions such as rising, falling, or steady-state are incorporated with the temporal features. The proposed predictive models can predict the epistemic states with significantly improved accuracy: correlation coefficient (CoERR) for Agreement is 0.827, for Concentration 0.901, for Thoughtful 0.794, for Certain 0.854, and for Interest 0.913.Comment: Accepted for Publication in Multimedia Tools and Application, Special Issue: Socio-Affective Technologie

Time-delay neural network for continuous emotional dimension prediction from facial expression sequences

"(c) 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works."Automatic continuous affective state prediction from naturalistic facial expression is a very challenging research topic but very important in human-computer interaction. One of the main challenges is modeling the dynamics that characterize naturalistic expressions. In this paper, a novel two-stage automatic system is proposed to continuously predict affective dimension values from facial expression videos. In the first stage, traditional regression methods are used to classify each individual video frame, while in the second stage, a Time-Delay Neural Network (TDNN) is proposed to model the temporal relationships between consecutive predictions. The two-stage approach separates the emotional state dynamics modeling from an individual emotional state prediction step based on input features. In doing so, the temporal information used by the TDNN is not biased by the high variability between features of consecutive frames and allows the network to more easily exploit the slow changing dynamics between emotional states. The system was fully tested and evaluated on three different facial expression video datasets. Our experimental results demonstrate that the use of a two-stage approach combined with the TDNN to take into account previously classified frames significantly improves the overall performance of continuous emotional state estimation in naturalistic facial expressions. The proposed approach has won the affect recognition sub-challenge of the third international Audio/Visual Emotion Recognition Challenge (AVEC2013)1

Exploiting Group Structures to Infer Social Interactions From Videos

In this thesis, we consider the task of inferring the social interactions between humans by analyzing multi-modal data. Specifically, we attempt to solve some of the problems in interaction analysis, such as long-term deception detection, political deception detection, and impression prediction. In this work, we emphasize the importance of using knowledge about the group structure of the analyzed interactions. Previous works on the matter mostly neglected this aspect and analyzed a single subject at a time. Using the new Resistance dataset, collected by our collaborators, we approach the problem of long-term deception detection by designing a class of histogram-based features and a novel class of meta-features we callLiarRank. We develop a LiarOrNot model to identify spies in Resistance videos. We achieve AUCs of over 0.70 outperforming our baselines by 3% and human judges by 12%. For the problem of political deception, we first collect a dataset of videos and transcripts of 76 politicians from 18 countries making truthful and deceptive statements. We call it the Global Political Deception Dataset. We then show how to analyze the statements in a broader context by building a Video-Article-Topic graph. From this graph, we create a novel class of features called Deception Score that captures how controversial each topic is and how it affects the truthfulness of each statement. We show that our approach achieves 0.775 AUC outperforming competing baselines. Finally, we use the Resistance data to solve the problem of dyadic impression prediction. Our proposed Dyadic Impression Prediction System (DIPS) contains four major innovations: a novel class of features called emotion ranks, sign imbalance features derived from signed graphs theory, a novel method to align the facial expressions of subjects, and finally, we propose the concept of a multilayered stochastic network we call Temporal Delayed Network. Our DIPS architecture beats eight baselines from the literature, yielding statistically significant improvements of 19.9-30.8% in AUC

End-to-end Audiovisual Speech Activity Detection with Bimodal Recurrent Neural Models

Speech activity detection (SAD) plays an important role in current speech processing systems, including automatic speech recognition (ASR). SAD is particularly difficult in environments with acoustic noise. A practical solution is to incorporate visual information, increasing the robustness of the SAD approach. An audiovisual system has the advantage of being robust to different speech modes (e.g., whisper speech) or background noise. Recent advances in audiovisual speech processing using deep learning have opened opportunities to capture in a principled way the temporal relationships between acoustic and visual features. This study explores this idea proposing a \emph{bimodal recurrent neural network} (BRNN) framework for SAD. The approach models the temporal dynamic of the sequential audiovisual data, improving the accuracy and robustness of the proposed SAD system. Instead of estimating hand-crafted features, the study investigates an end-to-end training approach, where acoustic and visual features are directly learned from the raw data during training. The experimental evaluation considers a large audiovisual corpus with over 60.8 hours of recordings, collected from 105 speakers. The results demonstrate that the proposed framework leads to absolute improvements up to 1.2% under practical scenarios over a VAD baseline using only audio implemented with deep neural network (DNN). The proposed approach achieves 92.7% F1-score when it is evaluated using the sensors from a portable tablet under noisy acoustic environment, which is only 1.0% lower than the performance obtained under ideal conditions (e.g., clean speech obtained with a high definition camera and a close-talking microphone).Comment: Submitted to Speech Communicatio

Sensing, interpreting, and anticipating human social behaviour in the real world

Low-level nonverbal social signals like glances, utterances, facial expressions and body language are central to human communicative situations and have been shown to be connected to important high-level constructs, such as emotions, turn-taking, rapport, or leadership. A prerequisite for the creation of social machines that are able to support humans in e.g. education, psychotherapy, or human resources is the ability to automatically sense, interpret, and anticipate human nonverbal behaviour. While promising results have been shown in controlled settings, automatically analysing unconstrained situations, e.g. in daily-life settings, remains challenging. Furthermore, anticipation of nonverbal behaviour in social situations is still largely unexplored. The goal of this thesis is to move closer to the vision of social machines in the real world. It makes fundamental contributions along the three dimensions of sensing, interpreting and anticipating nonverbal behaviour in social interactions. First, robust recognition of low-level nonverbal behaviour lays the groundwork for all further analysis steps. Advancing human visual behaviour sensing is especially relevant as the current state of the art is still not satisfactory in many daily-life situations. While many social interactions take place in groups, current methods for unsupervised eye contact detection can only handle dyadic interactions. We propose a novel unsupervised method for multi-person eye contact detection by exploiting the connection between gaze and speaking turns. Furthermore, we make use of mobile device engagement to address the problem of calibration drift that occurs in daily-life usage of mobile eye trackers. Second, we improve the interpretation of social signals in terms of higher level social behaviours. In particular, we propose the first dataset and method for emotion recognition from bodily expressions of freely moving, unaugmented dyads. Furthermore, we are the first to study low rapport detection in group interactions, as well as investigating a cross-dataset evaluation setting for the emergent leadership detection task. Third, human visual behaviour is special because it functions as a social signal and also determines what a person is seeing at a given moment in time. Being able to anticipate human gaze opens up the possibility for machines to more seamlessly share attention with humans, or to intervene in a timely manner if humans are about to overlook important aspects of the environment. We are the first to propose methods for the anticipation of eye contact in dyadic conversations, as well as in the context of mobile device interactions during daily life, thereby paving the way for interfaces that are able to proactively intervene and support interacting humans.Blick, Gesichtsausdrücke, Körpersprache, oder Prosodie spielen als nonverbale Signale eine zentrale Rolle in menschlicher Kommunikation. Sie wurden durch vielzählige Studien mit wichtigen Konzepten wie Emotionen, Sprecherwechsel, Führung, oder der Qualität des Verhältnisses zwischen zwei Personen in Verbindung gebracht. Damit Menschen effektiv während ihres täglichen sozialen Lebens von Maschinen unterstützt werden können, sind automatische Methoden zur Erkennung, Interpretation, und Antizipation von nonverbalem Verhalten notwendig. Obwohl die bisherige Forschung in kontrollierten Studien zu ermutigenden Ergebnissen gekommen ist, bleibt die automatische Analyse nonverbalen Verhaltens in weniger kontrollierten Situationen eine Herausforderung. Darüber hinaus existieren kaum Untersuchungen zur Antizipation von nonverbalem Verhalten in sozialen Situationen. Das Ziel dieser Arbeit ist, die Vision vom automatischen Verstehen sozialer Situationen ein Stück weit mehr Realität werden zu lassen. Diese Arbeit liefert wichtige Beiträge zur autmatischen Erkennung menschlichen Blickverhaltens in alltäglichen Situationen. Obwohl viele soziale Interaktionen in Gruppen stattfinden, existieren unüberwachte Methoden zur Augenkontakterkennung bisher lediglich für dyadische Interaktionen. Wir stellen einen neuen Ansatz zur Augenkontakterkennung in Gruppen vor, welcher ohne manuelle Annotationen auskommt, indem er sich den statistischen Zusammenhang zwischen Blick- und Sprechverhalten zu Nutze macht. Tägliche Aktivitäten sind eine Herausforderung für Geräte zur mobile Augenbewegungsmessung, da Verschiebungen dieser Geräte zur Verschlechterung ihrer Kalibrierung führen können. In dieser Arbeit verwenden wir Nutzerverhalten an mobilen Endgeräten, um den Effekt solcher Verschiebungen zu korrigieren. Neben der Erkennung verbessert diese Arbeit auch die Interpretation sozialer Signale. Wir veröffentlichen den ersten Datensatz sowie die erste Methode zur Emotionserkennung in dyadischen Interaktionen ohne den Einsatz spezialisierter Ausrüstung. Außerdem stellen wir die erste Studie zur automatischen Erkennung mangelnder Verbundenheit in Gruppeninteraktionen vor, und führen die erste datensatzübergreifende Evaluierung zur Detektion von sich entwickelndem Führungsverhalten durch. Zum Abschluss der Arbeit präsentieren wir die ersten Ansätze zur Antizipation von Blickverhalten in sozialen Interaktionen. Blickverhalten hat die besondere Eigenschaft, dass es sowohl als soziales Signal als auch der Ausrichtung der visuellen Wahrnehmung dient. Somit eröffnet die Fähigkeit zur Antizipation von Blickverhalten Maschinen die Möglichkeit, sich sowohl nahtloser in soziale Interaktionen einzufügen, als auch Menschen zu warnen, wenn diese Gefahr laufen wichtige Aspekte der Umgebung zu übersehen. Wir präsentieren Methoden zur Antizipation von Blickverhalten im Kontext der Interaktion mit mobilen Endgeräten während täglicher Aktivitäten, als auch während dyadischer Interaktionen mittels Videotelefonie

Improving the accuracy of automatic facial expression recognition in speaking subjects with deep learning

When automatic facial expression recognition is applied to video sequences of speaking subjects, the recognition accuracy has been noted to be lower than with video sequences of still subjects. This effect known as the speaking effect arises during spontaneous conversations, and along with the affective expressions the speech articulation process influences facial configurations. In this work we question whether, aside from facial features, other cues relating to the articulation process would increase emotion recognition accuracy when added in input to a deep neural network model. We develop two neural networks that classify facial expressions in speaking subjects from the RAVDESS dataset, a spatio-temporal CNN and a GRU cell RNN. They are first trained on facial features only, and afterwards both on facial features and articulation related cues extracted from a model trained for lip reading, while varying the number of consecutive frames provided in input as well. We show that using DNNs the addition of features related to articulation increases classification accuracy up to 12%, the increase being greater with more consecutive frames provided in input to the model

Chinese Tones: Can You Listen With Your Eyes?:The Influence of Visual Information on Auditory Perception of Chinese Tones

CHINESE TONES: CAN YOU LISTEN WITH YOUR EYES? The Influence of Visual Information on Auditory Perception of Chinese Tones YUEQIAO HAN Summary Considering the fact that more than half of the languages spoken in the world (60%-70%) are so-called tone languages (Yip, 2002), and tone is notoriously difficult to learn for westerners, this dissertation focused on tone perception in Mandarin Chinese by tone-naïve speakers. Moreover, it has been shown that speech perception is more than just an auditory phenomenon, especially in situations when the speaker’s face is visible. Therefore, the aim of this dissertation is to also study the value of visual information (over and above that of acoustic information) in Mandarin tone perception for tone-naïve perceivers, in combination with other contextual (such as speaking style) and individual factors (such as musical background). Consequently, this dissertation assesses the relative strength of acoustic and visual information in tone perception and tone classification. In the first two empirical and exploratory studies in Chapter 2 and 3 , we set out to investigate to what extent tone-naïve perceivers are able to identify Mandarin Chinese tones in isolated words, and whether or not they can benefit from (seeing) the speakers’ face, and what the contribution is of a hyperarticulated speaking style, and/or their own musical experience. Respectively, in Chapter 2 we investigated the effect of visual cues (comparing audio-only with audio-visual presentations) and speaking style (comparing a natural speaking style with a teaching speaking style) on the perception of Mandarin tones by tone-naïve listeners, looking both at the relative strength of these two factors and their possible interactions; Chapter 3 was concerned with the effects of musicality of the participants (combined with modality) on Mandarin tone perception. In both of these studies, a Mandarin Chinese tone identification experiment was conducted: native speakers of a non-tonal language were asked to distinguish Mandarin Chinese tones based on audio (-only) or video (audio-visual) materials. In order to include variations, the experimental stimuli were recorded using four different speakers in imagined natural and teaching speaking scenarios. The proportion of correct responses (and average reaction times) of the participants were reported. The tone identification experiment presented in Chapter 2 showed that the video conditions (audio-visual natural and audio-visual teaching) resulted in an overall higher accuracy in tone perception than the auditory-only conditions (audio-only natural and audio-only teaching), but no better performance was observed in the audio-visual conditions in terms of reaction time, compared to the auditory-only conditions. Teaching style turned out to make no difference on the speed or accuracy of Mandarin tone perception (as compared to a natural speaking style). Further on, we presented the same experimental materials and procedure in Chapter 3 , but now with musicians and non-musicians as participants. The Goldsmith Musical Sophistication Index (Gold-MSI) was used to assess the musical aptitude of the participants. The data showed that overall, musicians outperformed non-musicians in the tone identification task in both auditory-visual and auditory-only conditions. Both groups identified tones more accurately in the auditory-visual conditions than in the auditory-only conditions. These results provided further evidence for the view that the availability of visual cues along with auditory information is useful for people who have no knowledge of Mandarin Chinese tones when they need to learn to identify these tones. Out of all the musical skills measured by Gold-MSI, the amount of musical training was the only predictor that had an impact on the accuracy of Mandarin tone perception. These findings suggest that learning to perceive Mandarin tones benefits from musical expertise, and visual information can facilitate Mandarin tone identification, but mainly for tone-naïve non-musicians. In addition, performance differed by tone: musicality improves accuracy for every tone; some tones are easier to identify than others: in particular, the identification of tone 3 (a low-falling-rising) proved to be the easiest, while tone 4 (a high-falling tone) was the most difficult to identify for all participants. The results of the first two experiments presented in chapters 2 and 3 showed that adding visual cues to clear auditory information facilitated the tone identification for tone-naïve perceivers (there is a significantly higher accuracy in audio-visual condition(s) than in auditory-only condition(s)). This visual facilitation was unaffected by the presence of (hyperarticulated) speaking style or the musical skill of the participants. Moreover, variations in speakers and tones had effects on the accurate identification of Mandarin tones by tone-naïve perceivers. In Chapter 4 , we compared the relative contribution of auditory and visual information during Mandarin Chinese tone perception. More specifically, we aimed to answer two questions: firstly, whether or not there is audio-visual integration at the tone level (i.e., we explored perceptual fusion between auditory and visual information). Secondly, we studied how visual information affects tone perception for native speakers and non-native (tone-naïve) speakers. To do this, we constructed various tone combinations of congruent (e.g., an auditory tone 1 paired with a visual tone 1, written as AxVx) and incongruent (e.g., an auditory tone 1 paired with a visual tone 2, written as AxVy) auditory-visual materials and presented them to native speakers of Mandarin Chinese and speakers of tone-naïve languages. Accuracy, defined as the percentage correct identification of a tone based on its auditory realization, was reported. When comparing the relative contribution of auditory and visual information during Mandarin Chinese tone perception with congruent and incongruent auditory and visual Chinese material for native speakers of Chinese and non-tonal languages, we found that visual information did not significantly contribute to the tone identification for native speakers of Mandarin Chinese. When there is a discrepancy between visual cues and acoustic information, (native and tone-naïve) participants tend to rely more on the auditory input than on the visual cues. Unlike the native speakers of Mandarin Chinese, tone-naïve participants were significantly influenced by the visual information during their auditory-visual integration, and they identified tones more accurately in congruent stimuli than in incongruent stimuli. In line with our previous work, the tone confusion matrix showed that tone identification varies with individual tones, with tone 3 (the low-dipping tone) being the easiest one to identify, whereas tone 4 (the high-falling tone) was the most difficult one. The results did not show evidence for auditory-visual integration among native participants, while visual information was helpful for tone-naïve participants. However, even for this group, visual information only marginally increased the accuracy in the tone identification task, and this increase depended on the tone in question. Chapter 5 is another chapter that zooms in on the relative strength of auditory and visual information for tone-naïve perceivers, but from the aspect of tone classification. In this chapter, we studied the acoustic and visual features of the tones produced by native speakers of Mandarin Chinese. Computational models based on acoustic features, visual features and acoustic-visual features were constructed to automatically classify Mandarin tones. Moreover, this study examined what perceivers pick up (perception) from what a speaker does (production, facial expression) by studying both production and perception. To be more specific, this chapter set out to answer: (1) which acoustic and visual features of tones produced by native speakers could be used to automatically classify Mandarin tones. Furthermore, (2) whether or not the features used in tone production are similar to or different from the ones that have cue value for tone-naïve perceivers when they categorize tones; and (3) whether and how visual information (i.e., facial expression and facial pose) contributes to the classification of Mandarin tones over and above the information provided by the acoustic signal. To address these questions, the stimuli that had been recorded (and described in chapter 2) and the response data that had been collected (and reported on in chapter 3) were used. Basic acoustic and visual features were extracted. Based on them, we used Random Forest classification to identify the most important acoustic and visual features for classifying the tones. The classifiers were trained on produced tone classification (given a set of auditory and visual features, predict the produced tone) and on perceived/responded tone classification (given a set of features, predict the corresponding tone as identified by the participant). The results showed that acoustic features outperformed visual features for tone classification, both for the classification of the produced and the perceived tone. However, tone-naïve perceivers did revert to the use of visual information in certain cases (when they gave wrong responses). So, visual information does not seem to play a significant role in native speakers’ tone production, but tone-naïve perceivers do sometimes consider visual information in their tone identification. These findings provided additional evidence that auditory information is more important than visual information in Mandarin tone perception and tone classification. Notably, visual features contributed to the participants’ erroneous performance. This suggests that visual information actually misled tone-naïve perceivers in their task of tone identification. To some extent, this is consistent with our claim that visual cues do influence tone perception. In addition, the ranking of the auditory features and visual features in tone perception showed that the factor perceiver (i.e., the participant) was responsible for the largest amount of variance explained in the responses by our tone-naïve participants, indicating the importance of individual differences in tone perception. To sum up, perceivers who do not have tone in their language background tend to make use of visual cues from the speakers’ faces for their perception of unknown tones (Mandarin Chinese in this dissertation), in addition to the auditory information they clearly also use. However, auditory cues are still the primary source they rely on. There is a consistent finding across the studies that the variations between tones, speakers and participants have an effect on the accuracy of tone identification for tone-naïve speaker