A review of affective computing: From unimodal analysis to multimodal fusion

Affective computing is an emerging interdisciplinary research field bringing together researchers and practitioners from various fields, ranging from artificial intelligence, natural language processing, to cognitive and social sciences. With the proliferation of videos posted online (e.g., on YouTube, Facebook, Twitter) for product reviews, movie reviews, political views, and more, affective computing research has increasingly evolved from conventional unimodal analysis to more complex forms of multimodal analysis. This is the primary motivation behind our first of its kind, comprehensive literature review of the diverse field of affective computing. Furthermore, existing literature surveys lack a detailed discussion of state of the art in multimodal affect analysis frameworks, which this review aims to address. Multimodality is defined by the presence of more than one modality or channel, e.g., visual, audio, text, gestures, and eye gage. In this paper, we focus mainly on the use of audio, visual and text information for multimodal affect analysis, since around 90% of the relevant literature appears to cover these three modalities. Following an overview of different techniques for unimodal affect analysis, we outline existing methods for fusing information from different modalities. As part of this review, we carry out an extensive study of different categories of state-of-the-art fusion techniques, followed by a critical analysis of potential performance improvements with multimodal analysis compared to unimodal analysis. A comprehensive overview of these two complementary fields aims to form the building blocks for readers, to better understand this challenging and exciting research field

Multimodal interaction with mobile devices : fusing a broad spectrum of modality combinations

This dissertation presents a multimodal architecture for use in mobile scenarios such as shopping and navigation. It also analyses a wide range of feasible modality input combinations for these contexts. For this purpose, two interlinked demonstrators were designed for stand-alone use on mobile devices. Of particular importance was the design and implementation of a modality fusion module capable of combining input from a range of communication modes like speech, handwriting, and gesture. The implementation is able to account for confidence value biases arising within and between modalities and also provides a method for resolving semantically overlapped input. Tangible interaction with real-world objects and symmetric multimodality are two further themes addressed in this work. The work concludes with the results from two usability field studies that provide insight on user preference and modality intuition for different modality combinations, as well as user acceptance for anthropomorphized objects.Diese Dissertation präsentiert eine multimodale Architektur zum Gebrauch in mobilen Umständen wie z. B. Einkaufen und Navigation. Außerdem wird ein großes Gebiet von möglichen modalen Eingabekombinationen zu diesen Umständen analysiert. Um das in praktischer Weise zu demonstrieren, wurden zwei teilweise gekoppelte Vorführungsprogramme zum 'stand-alone'; Gebrauch auf mobilen Geräten entworfen. Von spezieller Wichtigkeit war der Entwurf und die Ausführung eines Modalitäts-fusion Modul, das die Kombination einer Reihe von Kommunikationsarten wie Sprache, Handschrift und Gesten ermöglicht. Die Ausführung erlaubt die Veränderung von Zuverlässigkeitswerten innerhalb einzelner Modalitäten und außerdem ermöglicht eine Methode um die semantisch überlappten Eingaben auszuwerten. Wirklichkeitsnaher Dialog mit aktuellen Objekten und symmetrische Multimodalität sind zwei weitere Themen die in dieser Arbeit behandelt werden. Die Arbeit schließt mit Resultaten von zwei Feldstudien, die weitere Einsicht erlauben über die bevorzugte Art verschiedener Modalitätskombinationen, sowie auch über die Akzeptanz von anthropomorphisierten Objekten

Multimodal interaction with mobile devices : fusing a broad spectrum of modality combinations

This dissertation presents a multimodal architecture for use in mobile scenarios such as shopping and navigation. It also analyses a wide range of feasible modality input combinations for these contexts. For this purpose, two interlinked demonstrators were designed for stand-alone use on mobile devices. Of particular importance was the design and implementation of a modality fusion module capable of combining input from a range of communication modes like speech, handwriting, and gesture. The implementation is able to account for confidence value biases arising within and between modalities and also provides a method for resolving semantically overlapped input. Tangible interaction with real-world objects and symmetric multimodality are two further themes addressed in this work. The work concludes with the results from two usability field studies that provide insight on user preference and modality intuition for different modality combinations, as well as user acceptance for anthropomorphized objects.Diese Dissertation präsentiert eine multimodale Architektur zum Gebrauch in mobilen Umständen wie z. B. Einkaufen und Navigation. Außerdem wird ein großes Gebiet von möglichen modalen Eingabekombinationen zu diesen Umständen analysiert. Um das in praktischer Weise zu demonstrieren, wurden zwei teilweise gekoppelte Vorführungsprogramme zum \u27stand-alone\u27; Gebrauch auf mobilen Geräten entworfen. Von spezieller Wichtigkeit war der Entwurf und die Ausführung eines Modalitäts-fusion Modul, das die Kombination einer Reihe von Kommunikationsarten wie Sprache, Handschrift und Gesten ermöglicht. Die Ausführung erlaubt die Veränderung von Zuverlässigkeitswerten innerhalb einzelner Modalitäten und außerdem ermöglicht eine Methode um die semantisch überlappten Eingaben auszuwerten. Wirklichkeitsnaher Dialog mit aktuellen Objekten und symmetrische Multimodalität sind zwei weitere Themen die in dieser Arbeit behandelt werden. Die Arbeit schließt mit Resultaten von zwei Feldstudien, die weitere Einsicht erlauben über die bevorzugte Art verschiedener Modalitätskombinationen, sowie auch über die Akzeptanz von anthropomorphisierten Objekten

Multimodal Computational Attention for Scene Understanding

Robotic systems have limited computational capacities. Hence, computational attention models are important to focus on specific stimuli and allow for complex cognitive processing. For this purpose, we developed auditory and visual attention models that enable robotic platforms to efficiently explore and analyze natural scenes. To allow for attention guidance in human-robot interaction, we use machine learning to integrate the influence of verbal and non-verbal social signals into our models

The Effects Of Multimodal Feedback And Age On A Mouse Pointing Task

As the beneficial aspects of computers become more apparent to the elderly population and the baby boom generation moves into later adulthood there is opportunity to increase performance for older computer users. Performance decrements that occur naturally to the motor skills of older adults have shown to have a negative effect on interactions with indirect-manipulation devices, such as computer mice (Murata & Iwase, 2005). Although, a mouse will always have the traits of an indirect-manipulation interaction, the inclusion of additional sensory feedback likely increases the saliency of the task to the real world resulting in increases in performance (Biocca et al., 2002). There is strong evidence for a bimodal advantage that is present in people of all ages; additionally there is also very strong evidence that older adults are a group that uses extra sensory information to increase their everyday interactions with the environment (Cienkowski & Carney, 2002; Thompson & Malloy, 2004). This study examined the effects of having multimodal feedback (i.e., visual cues, auditory cues, and tactile cues) present during a target acquisition mouse task for young, middle-aged, and older experienced computer users. This research examined the performance and subjective attitudes when performing a mouse based pointing task when different combinations of the modalities were present. The inclusion of audio or tactile cues during the task had the largest positive effect on performance, resulting in significantly quicker task completion for all of the computer users. The presence of audio or tactile cues increased performance for all of the age groups; however the performance of the older adults tended to be positively influenced more than the other age groups due the inclusion of these modalities. Additionally, the presence of visual cues did not have as strong of an effect on overall performance in comparison to the other modalities. Although the presence of audio and tactile feedback both increased performance there was evidence of a speed accuracy trade-off. Both the audio and tactile conditions resulted in a significantly higher number of misses in comparison to having no additional cues or visual cues present. So, while the presence of audio and tactile feedback improved the speed at which the task could be completed this occurred due to a sacrifice in accuracy. Additionally, this study shows strong evidence that audio and tactile cues are undesirable to computer users. The findings of this research are important to consider prior to adding extra sensory modalities to any type of user interface. The idea that additional feedback is always better may not always hold true if the feedback is found to be distracting, annoying, or negatively affects accuracy, as was found in this study with audio and tactile cues

Multimodal dynamics : self-supervised learning in perceptual and motor systems

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (leaves 178-192).This thesis presents a self-supervised framework for perceptual and motor learning based upon correlations in different sensory modalities. The brain and cognitive sciences have gathered an enormous body of neurological and phenomenological evidence in the past half century demonstrating the extraordinary degree of interaction between sensory modalities during the course of ordinary perception. We develop a framework for creating artificial perceptual systems that draws on these findings, where the primary architectural motif is the cross-modal transmission of perceptual information to enhance each sensory channel individually. We present self-supervised algorithms for learning perceptual grounding, intersensory influence, and sensorymotor coordination, which derive training signals from internal cross-modal correlations rather than from external supervision. Our goal is to create systems that develop by interacting with the world around them, inspired by development in animals. We demonstrate this framework with: (1) a system that learns the number and structure of vowels in American English by simultaneously watching and listening to someone speak. The system then cross-modally clusters the correlated auditory and visual data.(cont.) It has no advance linguistic knowledge and receives no information outside of its sensory channels. This work is the first unsupervised acquisition of phonetic structure of which we are aware, outside of that done by human infants. (2) a system that learns to sing like a zebra finch, following the developmental stages of a juvenile zebra finch. It first learns the song of an adult male and then listens to its own initially nascent attempts at mimicry through an articulatory synthesizer. In acquiring the birdsong to which it was initially exposed, this system demonstrates self-supervised sensorimotor learning. It also demonstrates afferent and efferent equivalence - the system learns motor maps with the same computational framework used for learning sensory maps.by Michael Harlan Coen.Ph.D

A novel approach for multimodal graph dimensionality reduction

This thesis deals with the problem of multimodal dimensionality reduction (DR), which arises when the input objects, to be mapped on a low-dimensional space, consist of multiple vectorial representations, instead of a single one. Herein, the problem is addressed in two alternative manners. One is based on the traditional notion of modality fusion, but using a novel approach to determine the fusion weights. In order to optimally fuse the modalities, the known graph embedding DR framework is extended to multiple modalities by considering a weighted sum of the involved affinity matrices. The weights of the sum are automatically calculated by minimizing an introduced notion of inconsistency of the resulting multimodal affinity matrix. The other manner for dealing with the problem is an approach to consider all modalities simultaneously, without fusing them, which has the advantage of minimal information loss due to fusion. In order to avoid fusion, the problem is viewed as a multi-objective optimization problem. The multiple objective functions are defined based on graph representations of the data, so that their individual minimization leads to dimensionality reduction for each modality separately. The aim is to combine the multiple modalities without the need to assign importance weights to them, or at least postpone such an assignment as a last step. The proposed approaches were experimentally tested in mapping multimedia data on low-dimensional spaces for purposes of visualization, classification and clustering. The no-fusion approach, namely Multi-objective DR, was able to discover mappings revealing the structure of all modalities simultaneously, which cannot be discovered by weight-based fusion methods. However, it results in a set of optimal trade-offs, from which one needs to be selected, which is not trivial. The optimal-fusion approach, namely Multimodal Graph Embedding DR, is able to easily extend unimodal DR methods to multiple modalities, but depends on the limitations of the unimodal DR method used. Both the no-fusion and the optimal-fusion approaches were compared to state-of-the-art multimodal dimensionality reduction methods and the comparison showed performance improvement in visualization, classification and clustering tasks. The proposed approaches were also evaluated for different types of problems and data, in two diverse application fields, a visual-accessibility-enhanced search engine and a visualization tool for mobile network security data. The results verified their applicability in different domains and suggested promising directions for future advancements.Open Acces

COMPUTATIONAL MODELING OF MULITSENSORY PROCESSING USING NETWORK OF SPIKING NEURONS

Multisensory processing in the brain underlies a wide variety of perceptual phenomena, but little is known about the underlying mechanisms of how multisensory neurons are generated and how the neurons integrate sensory information from environmental events. This lack of knowledge is due to the difficulty of biological experiments to manipulate and test the characteristics of multisensory processing. By using a computational model of multisensory processing this research seeks to provide insight into the mechanisms of multisensory processing. From a computational perspective, modeling of brain functions involves not only the computational model itself but also the conceptual definition of the brain functions, the analysis of correspondence between the model and the brain, and the generation of new biologically plausible insights and hypotheses. In this research, the multisensory processing is conceptually defined as the effect of multisensory convergence on the generation of multisensory neurons and their integrated response products, i.e., multisensory integration. Thus, the computational model is the implementation of the multisensory convergence and the simulation of the neural processing acting upon the convergence. Next, the most important step in the modeling is analysis of how well the model represents the target, i.e., brain function. It is also related to validation of the model. One of the intuitive and powerful ways of validating the model is to apply methods standard to neuroscience for analyzing the results obtained from the model. In addition, methods such as statistical and graph-theoretical analyses are used to confirm the similarity between the model and the brain. This research takes both approaches to provide analyses from many different perspectives. Finally, the model and its simulations provide insight into multisensory processing, generating plausible hypotheses, which will need to be confirmed by real experimentation

Building Embodied Conversational Agents:Observations on human nonverbal behaviour as a resource for the development of artificial characters

"Wow this is so cool!" This is what I most probably yelled, back in the 90s, when my first computer program on our MSX computer turned out to do exactly what I wanted it to do. The program contained the following instruction: COLOR 10(1.1) After hitting enter, it would change the screen color from light blue to dark yellow. A few years after that experience, Microsoft Windows was introduced. Windows came with an intuitive graphical user interface that was designed to allow all people, so also those who would not consider themselves to be experienced computer addicts, to interact with the computer. This was a major step forward in human-computer interaction, as from that point forward no complex programming skills were required anymore to perform such actions as adapting the screen color. Changing the background was just a matter of pointing the mouse to the desired color on a color palette. "Wow this is so cool!". This is what I shouted, again, 20 years later. This time my new smartphone successfully skipped to the next song on Spotify because I literally told my smartphone, with my voice, to do so. Being able to operate your smartphone with natural language through voice-control can be extremely handy, for instance when listening to music while showering. Again, the option to handle a computer with voice instructions turned out to be a significant optimization in human-computer interaction. From now on, computers could be instructed without the use of a screen, mouse or keyboard, and instead could operate successfully simply by telling the machine what to do. In other words, I have personally witnessed how, within only a few decades, the way people interact with computers has changed drastically, starting as a rather technical and abstract enterprise to becoming something that was both natural and intuitive, and did not require any advanced computer background. Accordingly, while computers used to be machines that could only be operated by technically-oriented individuals, they had gradually changed into devices that are part of many people’s household, just as much as a television, a vacuum cleaner or a microwave oven. The introduction of voice control is a significant feature of the newer generation of interfaces in the sense that these have become more "antropomorphic" and try to mimic the way people interact in daily life, where indeed the voice is a universally used device that humans exploit in their exchanges with others. The question then arises whether it would be possible to go even one step further, where people, like in science-fiction movies, interact with avatars or humanoid robots, whereby users can have a proper conversation with a computer-simulated human that is indistinguishable from a real human. An interaction with a human-like representation of a computer that behaves, talks and reacts like a real person would imply that the computer is able to not only produce and understand messages transmitted auditorily through the voice, but also could rely on the perception and generation of different forms of body language, such as facial expressions, gestures or body posture. At the time of writing, developments of this next step in human-computer interaction are in full swing, but the type of such interactions is still rather constrained when compared to the way humans have their exchanges with other humans. It is interesting to reflect on how such future humanmachine interactions may look like. When we consider other products that have been created in history, it sometimes is striking to see that some of these have been inspired by things that can be observed in our environment, yet at the same do not have to be exact copies of those phenomena. For instance, an airplane has wings just as birds, yet the wings of an airplane do not make those typical movements a bird would produce to fly. Moreover, an airplane has wheels, whereas a bird has legs. At the same time, an airplane has made it possible for a humans to cover long distances in a fast and smooth manner in a way that was unthinkable before it was invented. The example of the airplane shows how new technologies can have "unnatural" properties, but can nonetheless be very beneficial and impactful for human beings. This dissertation centers on this practical question of how virtual humans can be programmed to act more human-like. The four studies presented in this dissertation all have the equivalent underlying question of how parts of human behavior can be captured, such that computers can use it to become more human-like. Each study differs in method, perspective and specific questions, but they are all aimed to gain insights and directions that would help further push the computer developments of human-like behavior and investigate (the simulation of) human conversational behavior. The rest of this introductory chapter gives a general overview of virtual humans (also known as embodied conversational agents), their potential uses and the engineering challenges, followed by an overview of the four studies