Predicting continuous conflict perception with Bayesian Gaussian processes

Conflict is one of the most important phenomena of social life, but it is still largely neglected by the computing community. This work proposes an approach that detects common conversational social signals (loudness, overlapping speech, etc.) and predicts the conflict level perceived by human observers in continuous, non-categorical terms. The proposed regression approach is fully Bayesian and it adopts Automatic Relevance Determination to identify the social signals that influence most the outcome of the prediction. The experiments are performed over the SSPNet Conflict Corpus, a publicly available collection of 1430 clips extracted from televised political debates (roughly 12 hours of material for 138 subjects in total). The results show that it is possible to achieve a correlation close to 0.8 between actual and predicted conflict perception

ConflictNET: End-to-End Learning for Speech-Based Conflict Intensity Estimation

Computational paralinguistics aims to infer human emotions, personality traits and behavioural patterns from speech signals. In particular, verbal conflict is an important example of human-interaction behaviour, whose detection would enable monitoring and feedback in a variety of applications. The majority of methods for detection and intensity estimation of verbal conflict apply off-the-shelf classifiers/regressors to generic hand-crafted acoustic features. Generating conflict-specific features requires refinement steps and the availability of metadata, such as the number of speakers and their speech overlap duration. Moreover, most techniques treat feature extraction and regression as independent modules, which require separate training and parameter tuning. To address these limitations, we propose the first end-to-end convolutional-recurrent neural network architecture that learns conflict-specific features directly from raw speech waveforms, without using explicit domain knowledge or metadata. Additionally, to selectively focus the model on portions of speech containing verbal conflict instances, we include a global attention interface that learns the alignment between layers of the recurrent network. Experimental results on the SSPNet Conflict Corpus show that our end-to-end architecture achieves state-of-the-art performance in terms of Pearson Correlation Coefficient

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

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

Affective Speech Recognition

Speech, as a medium of interaction, carries two different streams of information. Whereas one stream carries explicit messages, the other one contains implicit information about speakers themselves. Affective speech recognition is a set of theories and tools that intend to automate unfolding the part of the implicit stream that has to do with humans emotion. Application of affective speech recognition is to human computer interaction; a machine that is able to recognize humans emotion could engage the user in a more effective interaction. This thesis proposes a set of analyses and methodologies that advance automatic recognition of affect from speech. The proposed solution spans two dimensions of the problem: speech signal processing, and statistical learning. At the speech signal processing dimension, extraction of speech low-level descriptors is dis- cussed, and a set of descriptors that exploit the spectrum of the signal are proposed, which have shown to be particularly practical for capturing affective qualities of speech. Moreover, consider- ing the non-stationary property of the speech signal, further proposed is a measure of dynamicity that captures that property of speech by quantifying changes of the signal over time. Furthermore, based on the proposed set of low-level descriptors, it is shown that individual human beings are different in conveying emotions, and that parts of the spectrum that hold the affective information are different from one person to another. Therefore, the concept of emotion profile is proposed that formalizes those differences by taking into account different factors such as cultural and gender-specific differences, as well as those distinctions that have to do with individual human beings. At the statistical learning dimension, variable selection is performed to identify speech features that are most imperative to extracting affective information. In doing so, low-level descriptors are distinguished from statistical functionals, therefore, effectiveness of each of the two are studied dependently and independently. The major importance of variable selection as a standalone component of a solution is to real-time application of affective speech recognition. Although thousands of speech features are commonly used to tackle this problem in theory, extracting that many features in a real-time manner is unrealistic, especially for mobile applications. Results of the conducted investigations show that the required number of speech features is far less than the number that is commonly used in the literature of the problem. At the core of an affective speech recognition solution is a statistical model that uses speech features to recognize emotions. Such a model comes with a set of parameters that are estimated through a learning process. Proposed in this thesis is a learning algorithm, developed based on the notion of Hilbert-Schmidt independence criterion and named max-dependence regression, that maximizes the dependence between predicted and actual values of affective qualities. Pearson’s correlation coefficient is commonly used as the measure of goodness of a fit in the literature of affective computing, therefore max-dependence regression is proposed to make the learning and hypothesis testing criteria consistent with one another. Results of this research show that doing so results in higher prediction accuracy. Lastly, sparse representation for affective speech datasets is considered in this thesis. For this purpose, the application of a dictionary learning algorithm based on Hilbert-Schmidt independence criterion is proposed. Dictionary learning is used to identify the most important bases of the data in order to improve the generalization capability of the proposed solution to affective speech recognition. Based on the dictionary learning approach of choice, fusion of feature vectors is proposed. It is shown that sparse representation leads to higher generalization capability for affective speech recognition

Automatic Emotion Recognition in Children with Autism: A Systematic Literature Review

© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).The automatic emotion recognition domain brings new methods and technologies that might be used to enhance therapy of children with autism. The paper aims at the exploration of methods and tools used to recognize emotions in children. It presents a literature review study that was performed using a systematic approach and PRISMA methodology for reporting quantitative and qualitative results. Diverse observation channels and modalities are used in the analyzed studies, including facial expressions, prosody of speech, and physiological signals. Regarding representation models, the basic emotions are the most frequently recognized, especially happiness, fear, and sadness. Both single-channel and multichannel approaches are applied, with a preference for the first one. For multimodal recognition, early fusion was the most frequently applied. SVM and neural networks were the most popular for building classifiers. Qualitative analysis revealed important clues on participant group construction and the most common combinations of modalities and methods. All channels are reported to be prone to some disturbance, and as a result, information on a specific symptoms of emotions might be temporarily or permanently unavailable. The challenges of proper stimuli, labelling methods, and the creation of open datasets were also identified.Peer reviewedFinal Published versio

Automated screening methods for mental and neuro-developmental disorders

Mental and neuro-developmental disorders such as depression, bipolar disorder, and autism spectrum disorder (ASD) are critical healthcare issues which affect a large number of people. Depression, according to the World Health Organisation, is the largest cause of disability worldwide and affects more than 300 million people. Bipolar disorder affects more than 60 million individuals worldwide. ASD, meanwhile, affects more than 1 in 100 people in the UK. Not only do these disorders adversely affect the quality of life of affected individuals, they also have a significant economic impact. While brute-force approaches are potentially useful for learning new features which could be representative of these disorders, such approaches may not be best suited for developing robust screening methods. This is due to a myriad of confounding factors, such as the age, gender, cultural background, and socio-economic status, which can affect social signals of individuals in a similar way as the symptoms of these disorders. Brute-force approaches may learn to exploit effects of these confounding factors on social signals in place of effects due to mental and neuro-developmental disorders. The main objective of this thesis is to develop, investigate, and propose computational methods to screen for mental and neuro-developmental disorders in accordance with descriptions given in the Diagnostic and Statistical Manual (DSM). The DSM manual is a guidebook published by the American Psychiatric Association which offers common language on mental disorders. Our motivation is to alleviate, to an extent, the possibility of machine learning algorithms picking up one of the confounding factors to optimise performance for the dataset – something which we do not find uncommon in research literature. To this end, we introduce three new methods for automated screening for depression from audio/visual recordings, namely: turbulence features, craniofacial movement features, and Fisher Vector based representation of speech spectra. We surmise that psychomotor changes due to depression lead to uniqueness in an individual's speech pattern which manifest as sudden and erratic changes in speech feature contours. The efficacy of these features is demonstrated as part of our solution to Audio/Visual Emotion Challenge 2017 (AVEC 2017) on Depression severity prediction. We also detail a methodology to quantify specific craniofacial movements, which we hypothesised could be indicative of psychomotor retardation, and hence depression. The efficacy of craniofacial movement features is demonstrated using datasets from the 2014 and 2017 editions of AVEC Depression severity prediction challenges. Finally, using the dataset provided as part of AVEC 2016 Depression classification challenge, we demonstrate that differences between speech of individuals with and without depression can be quantified effectively using the Fisher Vector representation of speech spectra. For our work on automated screening of bipolar disorder, we propose methods to classify individuals with bipolar disorder into states of remission, hypo-mania, and mania. Here, we surmise that like depression, individuals with different levels of mania have certain uniqueness to their social signals. Based on this understanding, we propose the use of turbulence features for audio/visual social signals (i.e. speech and facial expressions). We also propose the use of Fisher Vectors to create a unified representation of speech in terms of prosody, voice quality, and speech spectra. These methods have been proposed as part of our solution to the AVEC 2018 Bipolar disorder challenge. In addition, we find that the task of automated screening for ASD is much more complicated. Here, confounding factors can easily overwhelm socials signals which are affected by ASD. We discuss, in the light of research literature and our experimental analysis, that significant collaborative work is required between computer scientists and clinicians to discern social signals which are robust to common confounding factors

Visual Scanning of Dynamic Affective Stimuli in Autism Spectrum Disorders

The accurate integration of audio-visual emotion cues is critical for social interactions and requires efficient processing of facial cues. Gaze behavior of typically developing (TD) individuals and individuals with autism spectrum disorders (ASD) was measured via eye-tracking during the perception of dynamic audio-visual emotion (DAVE) stimuli. This study provides information about the regions of the face sampled during an emotion perception task that is relatively more complex than those used in previous studies, providing both bimodal (auditory and visual) and dynamic (biological motion) cues. Results indicated that the ASD group was less accurate at emotion detection and demonstrated less of a visual-affective bias than TD individuals. Both groups displayed similar fixation patterns across regions during the perception of congruent audio-visual stimuli. However, between-group analyses revealed that fixation patterns differed significantly by facial regions during the perception of both congruent and incongruent movies together. In addition, fixation duration to critical regions (i.e., face, core, eyes) was negatively correlated with measures of ASD symptomatology and social impairment. Findings suggest weaknesses in the early integration of audio-visual information, automatic perception of emotion, and efficient detection of affective conflict in individuals with ASD. Implications for future research and social skills intervention programs are discussed

Gaze Fixation during the Perception of Visual and Auditory Affective Cues

The accurate integration of audio-visual emotion cues is critical for social interactions and requires efficient processing of facial cues. Gaze behavior of typically developing young adults was measured via eye-tracking during the perception of dynamic audio-visual emotion (DAVE) stimuli. Participants were able to identify basic emotions (angry, fearful, happy, neutral) and determine the congruence of facial expression and prosody. Perception of incongruent videos resulted in increased reaction times and emotion identification consistent with the facial expression. Participants consistently demonstrated a featural processing approach across all tasks, with a significant preference for the eyes. Evidence of hemispheric lateralization was indicated by preferential fixation to the left (happy, angry) or right eye (fearful). Fixation patterns differed according to the facially expressed emotion, with the pattern that emerged during fearful movies supporting the significance of automatic threat processing. Finally, fixation pattern during the perception of incongruent movies varied according to task instructions