14 research outputs found

    Affective Computing

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    This book provides an overview of state of the art research in Affective Computing. It presents new ideas, original results and practical experiences in this increasingly important research field. The book consists of 23 chapters categorized into four sections. Since one of the most important means of human communication is facial expression, the first section of this book (Chapters 1 to 7) presents a research on synthesis and recognition of facial expressions. Given that we not only use the face but also body movements to express ourselves, in the second section (Chapters 8 to 11) we present a research on perception and generation of emotional expressions by using full-body motions. The third section of the book (Chapters 12 to 16) presents computational models on emotion, as well as findings from neuroscience research. In the last section of the book (Chapters 17 to 22) we present applications related to affective computing

    Speech Recognition

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    Chapters in the first part of the book cover all the essential speech processing techniques for building robust, automatic speech recognition systems: the representation for speech signals and the methods for speech-features extraction, acoustic and language modeling, efficient algorithms for searching the hypothesis space, and multimodal approaches to speech recognition. The last part of the book is devoted to other speech processing applications that can use the information from automatic speech recognition for speaker identification and tracking, for prosody modeling in emotion-detection systems and in other speech processing applications that are able to operate in real-world environments, like mobile communication services and smart homes

    Deep Active Learning Explored Across Diverse Label Spaces

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    abstract: Deep learning architectures have been widely explored in computer vision and have depicted commendable performance in a variety of applications. A fundamental challenge in training deep networks is the requirement of large amounts of labeled training data. While gathering large quantities of unlabeled data is cheap and easy, annotating the data is an expensive process in terms of time, labor and human expertise. Thus, developing algorithms that minimize the human effort in training deep models is of immense practical importance. Active learning algorithms automatically identify salient and exemplar samples from large amounts of unlabeled data and can augment maximal information to supervised learning models, thereby reducing the human annotation effort in training machine learning models. The goal of this dissertation is to fuse ideas from deep learning and active learning and design novel deep active learning algorithms. The proposed learning methodologies explore diverse label spaces to solve different computer vision applications. Three major contributions have emerged from this work; (i) a deep active framework for multi-class image classication, (ii) a deep active model with and without label correlation for multi-label image classi- cation and (iii) a deep active paradigm for regression. Extensive empirical studies on a variety of multi-class, multi-label and regression vision datasets corroborate the potential of the proposed methods for real-world applications. Additional contributions include: (i) a multimodal emotion database consisting of recordings of facial expressions, body gestures, vocal expressions and physiological signals of actors enacting various emotions, (ii) four multimodal deep belief network models and (iii) an in-depth analysis of the effect of transfer of multimodal emotion features between source and target networks on classification accuracy and training time. These related contributions help comprehend the challenges involved in training deep learning models and motivate the main goal of this dissertation.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201

    Speech-driven Animation with Meaningful Behaviors

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    Conversational agents (CAs) play an important role in human computer interaction. Creating believable movements for CAs is challenging, since the movements have to be meaningful and natural, reflecting the coupling between gestures and speech. Studies in the past have mainly relied on rule-based or data-driven approaches. Rule-based methods focus on creating meaningful behaviors conveying the underlying message, but the gestures cannot be easily synchronized with speech. Data-driven approaches, especially speech-driven models, can capture the relationship between speech and gestures. However, they create behaviors disregarding the meaning of the message. This study proposes to bridge the gap between these two approaches overcoming their limitations. The approach builds a dynamic Bayesian network (DBN), where a discrete variable is added to constrain the behaviors on the underlying constraint. The study implements and evaluates the approach with two constraints: discourse functions and prototypical behaviors. By constraining on the discourse functions (e.g., questions), the model learns the characteristic behaviors associated with a given discourse class learning the rules from the data. By constraining on prototypical behaviors (e.g., head nods), the approach can be embedded in a rule-based system as a behavior realizer creating trajectories that are timely synchronized with speech. The study proposes a DBN structure and a training approach that (1) models the cause-effect relationship between the constraint and the gestures, (2) initializes the state configuration models increasing the range of the generated behaviors, and (3) captures the differences in the behaviors across constraints by enforcing sparse transitions between shared and exclusive states per constraint. Objective and subjective evaluations demonstrate the benefits of the proposed approach over an unconstrained model.Comment: 13 pages, 12 figures, 5 table

    [<sup>18</sup>F]fluorination of biorelevant arylboronic acid pinacol ester scaffolds synthesized by convergence techniques

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    Aim: The development of small molecules through convergent multicomponent reactions (MCR) has been boosted during the last decade due to the ability to synthesize, virtually without any side-products, numerous small drug-like molecules with several degrees of structural diversity.(1) The association of positron emission tomography (PET) labeling techniques in line with the “one-pot” development of biologically active compounds has the potential to become relevant not only for the evaluation and characterization of those MCR products through molecular imaging, but also to increase the library of radiotracers available. Therefore, since the [18F]fluorination of arylboronic acid pinacol ester derivatives tolerates electron-poor and electro-rich arenes and various functional groups,(2) the main goal of this research work was to achieve the 18F-radiolabeling of several different molecules synthesized through MCR. Materials and Methods: [18F]Fluorination of boronic acid pinacol esters was first extensively optimized using a benzaldehyde derivative in relation to the ideal amount of Cu(II) catalyst and precursor to be used, as well as the reaction solvent. Radiochemical conversion (RCC) yields were assessed by TLC-SG. The optimized radiolabeling conditions were subsequently applied to several structurally different MCR scaffolds comprising biologically relevant pharmacophores (e.g. β-lactam, morpholine, tetrazole, oxazole) that were synthesized to specifically contain a boronic acid pinacol ester group. Results: Radiolabeling with fluorine-18 was achieved with volumes (800 μl) and activities (≤ 2 GBq) compatible with most radiochemistry techniques and modules. In summary, an increase in the quantities of precursor or Cu(II) catalyst lead to higher conversion yields. An optimal amount of precursor (0.06 mmol) and Cu(OTf)2(py)4 (0.04 mmol) was defined for further reactions, with DMA being a preferential solvent over DMF. RCC yields from 15% to 76%, depending on the scaffold, were reproducibly achieved. Interestingly, it was noticed that the structure of the scaffolds, beyond the arylboronic acid, exerts some influence in the final RCC, with electron-withdrawing groups in the para position apparently enhancing the radiolabeling yield. Conclusion: The developed method with high RCC and reproducibility has the potential to be applied in line with MCR and also has a possibility to be incorporated in a later stage of this convergent “one-pot” synthesis strategy. Further studies are currently ongoing to apply this radiolabeling concept to fluorine-containing approved drugs whose boronic acid pinacol ester precursors can be synthesized through MCR (e.g. atorvastatin)

    Automatic Emotion Recognition: Quantifying Dynamics and Structure in Human Behavior.

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    Emotion is a central part of human interaction, one that has a huge influence on its overall tone and outcome. Today's human-centered interactive technology can greatly benefit from automatic emotion recognition, as the extracted affective information can be used to measure, transmit, and respond to user needs. However, developing such systems is challenging due to the complexity of emotional expressions and their dynamics in terms of the inherent multimodality between audio and visual expressions, as well as the mixed factors of modulation that arise when a person speaks. To overcome these challenges, this thesis presents data-driven approaches that can quantify the underlying dynamics in audio-visual affective behavior. The first set of studies lay the foundation and central motivation of this thesis. We discover that it is crucial to model complex non-linear interactions between audio and visual emotion expressions, and that dynamic emotion patterns can be used in emotion recognition. Next, the understanding of the complex characteristics of emotion from the first set of studies leads us to examine multiple sources of modulation in audio-visual affective behavior. Specifically, we focus on how speech modulates facial displays of emotion. We develop a framework that uses speech signals which alter the temporal dynamics of individual facial regions to temporally segment and classify facial displays of emotion. Finally, we present methods to discover regions of emotionally salient events in a given audio-visual data. We demonstrate that different modalities, such as the upper face, lower face, and speech, express emotion with different timings and time scales, varying for each emotion type. We further extend this idea into another aspect of human behavior: human action events in videos. We show how transition patterns between events can be used for automatically segmenting and classifying action events. Our experimental results on audio-visual datasets show that the proposed systems not only improve performance, but also provide descriptions of how affective behaviors change over time. We conclude this dissertation with the future directions that will innovate three main research topics: machine adaptation for personalized technology, human-human interaction assistant systems, and human-centered multimedia content analysis.PhDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/133459/1/yelinkim_1.pd

    Multimodal Video Analysis and Modeling

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    From recalling long forgotten experiences based on a familiar scent or on a piece of music, to lip reading aided conversation in noisy environments or travel sickness caused by mismatch of the signals from vision and the vestibular system, the human perception manifests countless examples of subtle and effortless joint adoption of the multiple senses provided to us by evolution. Emulating such multisensory (or multimodal, i.e., comprising multiple types of input modes or modalities) processing computationally offers tools for more effective, efficient, or robust accomplishment of many multimedia tasks using evidence from the multiple input modalities. Information from the modalities can also be analyzed for patterns and connections across them, opening up interesting applications not feasible with a single modality, such as prediction of some aspects of one modality based on another. In this dissertation, multimodal analysis techniques are applied to selected video tasks with accompanying modalities. More specifically, all the tasks involve some type of analysis of videos recorded by non-professional videographers using mobile devices.Fusion of information from multiple modalities is applied to recording environment classification from video and audio as well as to sport type classification from a set of multi-device videos, corresponding audio, and recording device motion sensor data. The environment classification combines support vector machine (SVM) classifiers trained on various global visual low-level features with audio event histogram based environment classification using k nearest neighbors (k-NN). Rule-based fusion schemes with genetic algorithm (GA)-optimized modality weights are compared to training a SVM classifier to perform the multimodal fusion. A comprehensive selection of fusion strategies is compared for the task of classifying the sport type of a set of recordings from a common event. These include fusion prior to, simultaneously with, and after classification; various approaches for using modality quality estimates; and fusing soft confidence scores as well as crisp single-class predictions. Additionally, different strategies are examined for aggregating the decisions of single videos to a collective prediction from the set of videos recorded concurrently with multiple devices. In both tasks multimodal analysis shows clear advantage over separate classification of the modalities.Another part of the work investigates cross-modal pattern analysis and audio-based video editing. This study examines the feasibility of automatically timing shot cuts of multi-camera concert recordings according to music-related cutting patterns learnt from professional concert videos. Cut timing is a crucial part of automated creation of multicamera mashups, where shots from multiple recording devices from a common event are alternated with the aim at mimicing a professionally produced video. In the framework, separate statistical models are formed for typical patterns of beat-quantized cuts in short segments, differences in beats between consecutive cuts, and relative deviation of cuts from exact beat times. Based on music meter and audio change point analysis of a new recording, the models can be used for synthesizing cut times. In a user study the proposed framework clearly outperforms a baseline automatic method with comparably advanced audio analysis and wins 48.2 % of comparisons against hand-edited videos

    Infinite Hidden Conditional Random Fields for the Recognition of Human Behaviour

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    While detecting and interpreting temporal patterns of nonverbal behavioral cues in a given context is a natural and often unconscious process for humans, it remains a rather difficult task for computer systems. In this thesis we are primarily motivated by the problem of recognizing expressions of high--level behavior, and specifically agreement and disagreement. We thoroughly dissect the problem by surveying the nonverbal behavioral cues that could be present during displays of agreement and disagreement; we discuss a number of methods that could be used or adapted to detect these suggested cues; we list some publicly available databases these tools could be trained on for the analysis of spontaneous, audiovisual instances of agreement and disagreement, we examine the few existing attempts at agreement and disagreement classification, and we discuss the challenges in automatically detecting agreement and disagreement. We present experiments that show that an existing discriminative graphical model, the Hidden Conditional Random Field (HCRF) is the best performing on this task. The HCRF is a discriminative latent variable model which has been previously shown to successfully learn the hidden structure of a given classification problem (provided an appropriate validation of the number of hidden states). We show here that HCRFs are also able to capture what makes each of these social attitudes unique. We present an efficient technique to analyze the concepts learned by the HCRF model and show that these coincide with the findings from social psychology regarding which cues are most prevalent in agreement and disagreement. Our experiments are performed on a spontaneous expressions dataset curated from real televised debates. The HCRF model outperforms conventional approaches such as Hidden Markov Models and Support Vector Machines. Subsequently, we examine existing graphical models that use Bayesian nonparametrics to have a countably infinite number of hidden states and adapt their complexity to the data at hand. We identify a gap in the literature that is the lack of a discriminative such graphical model and we present our suggestion for the first such model: an HCRF with an infinite number of hidden states, the Infinite Hidden Conditional Random Field (IHCRF). In summary, the IHCRF is an undirected discriminative graphical model for sequence classification and uses a countably infinite number of hidden states. We present two variants of this model. The first is a fully nonparametric model that relies on Hierarchical Dirichlet Processes and a Markov Chain Monte Carlo inference approach. The second is a semi--parametric model that uses Dirichlet Process Mixtures and relies on a mean--field variational inference approach. We show that both models are able to converge to a correct number of represented hidden states, and perform as well as the best finite HCRFs ---chosen via cross--validation--- for the difficult tasks of recognizing instances of agreement, disagreement, and pain in audiovisual sequences.Open Acces
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