Multiple Classifier Systems for the Classification of Audio-Visual Emotional States

Abstract. Research activities in the field of human-computer inter-action increasingly addressed the aspect of integrating some type of emotional intelligence. Human emotions are expressed through differ-ent modalities such as speech, facial expressions, hand or body gestures, and therefore the classification of human emotions should be considered as a multimodal pattern recognition problem. The aim of our paper is to investigate multiple classifier systems utilizing audio and visual features to classify human emotional states. For that a variety of features have been derived. From the audio signal the fundamental frequency, LPC-and MFCC coefficients, and RASTA-PLP have been used. In addition to that two types of visual features have been computed, namely form and motion features of intermediate complexity. The numerical evaluation has been performed on the four emotional labels Arousal, Expectancy, Power, Valence as defined in the AVEC data set. As classifier architec-tures multiple classifier systems are applied, these have been proven to be accurate and robust against missing and noisy data.

Behavioral Indicators on a Mobile Sensing Platform Predict Clinically Validated Psychiatric Symptoms of Mood and Anxiety Disorders

Background: There is a critical need for real-time tracking of behavioral indicators of mental disorders. Mobile sensing platforms that objectively and noninvasively collect, store, and analyze behavioral indicators have not yet been clinically validated or scalable. Objective: The aim of our study was to report on models of clinical symptoms for post-traumatic stress disorder (PTSD) and depression derived from a scalable mobile sensing platform. Methods: A total of 73 participants (67% [49/73] male, 48% [35/73] non-Hispanic white, 33% [24/73] veteran status) who reported at least one symptom of PTSD or depression completed a 12-week field trial. Behavioral indicators were collected through the noninvasive mobile sensing platform on participants’ mobile phones. Clinical symptoms were measured through validated clinical interviews with a licensed clinical social worker. A combination hypothesis and data-driven approach was used to derive key features for modeling symptoms, including the sum of outgoing calls, count of unique numbers texted, absolute distance traveled, dynamic variation of the voice, speaking rate, and voice quality. Participants also reported ease of use and data sharing concerns. Results: Behavioral indicators predicted clinically assessed symptoms of depression and PTSD (cross-validated area under the curve [AUC] for depressed mood=.74, fatigue=.56, interest in activities=.75, and social connectedness=.83). Participants reported comfort sharing individual data with physicians (Mean 3.08, SD 1.22), mental health providers (Mean 3.25, SD 1.39), and medical researchers (Mean 3.03, SD 1.36). Conclusions: Behavioral indicators passively collected through a mobile sensing platform predicted symptoms of depression and PTSD. The use of mobile sensing platforms can provide clinically validated behavioral indicators in real time; however, further validation of these models and this platform in large clinical samples is needed.United States. Defense Advanced Research Projects Agency (contract N66001-11-C-4094

Affective state level recognition in naturalistic facial and vocal expressions

Naturalistic affective expressions change at a rate much slower than the typical rate at which video or audio is recorded. This increases the probability that consecutive recorded instants of expressions represent the same affective content. In this paper, we exploit such a relationship to improve the recognition performance of continuous naturalistic affective expressions. Using datasets of naturalistic affective expressions (AVEC 2011 audio and video dataset, PAINFUL video dataset) continuously labeled over time and over different dimensions, we analyze the transitions between levels of those dimensions (e.g., transitions in pain intensity level). We use an information theory approach to show that the transitions occur very slowly and hence suggest modeling them as first-order Markov models. The dimension levels are considered to be the hidden states in the Hidden Markov Model (HMM) framework. Their discrete transition and emission matrices are trained by using the labels provided with the training set. The recognition problem is converted into a best path-finding problem to obtain the best hidden states sequence in HMMs. This is a key difference from previous use of HMMs as classifiers. Modeling of the transitions between dimension levels is integrated in a multistage approach, where the first level performs a mapping between the affective expression features and a soft decision value (e.g., an affective dimension level), and further classification stages are modeled as HMMs that refine that mapping by taking into account the temporal relationships between the output decision labels. The experimental results for each of the unimodal datasets show overall performance to be significantly above that of a standard classification system that does not take into account temporal relationships. In particular, the results on the AVEC 2011 audio dataset outperform all other systems presented at the international competition

Robust Methods for the Automatic Quantification and Prediction of Affect in Spoken Interactions

Emotional expression plays a key role in interactions as it communicates the necessary context needed for understanding the behaviors and intentions of individuals. Therefore, a speech-based Artificial Intelligence (AI) system that can recognize and interpret emotional expression has many potential applications with measurable impact to a variety of areas, including human-computer interaction (HCI) and healthcare. However, there are several factors that make speech emotion recognition (SER) a difficult task; these factors include: variability in speech data, variability in emotion annotations, and data sparsity. This dissertation explores methodologies for improving the robustness of the automatic recognition of emotional expression from speech by addressing the impacts of these factors on various aspects of the SER system pipeline. For addressing speech data variability in SER, we propose modeling techniques that improve SER performance by leveraging short-term dynamical properties of speech. Furthermore, we demonstrate how data augmentation improves SER robustness to speaker variations. Lastly, we discover that we can make more accurate predictions of emotion by considering the fine-grained interactions between the acoustic and lexical components of speech. For addressing the variability in emotion annotations, we propose SER modeling techniques that account for the behaviors of annotators (i.e., annotators' reaction delay) to improve time-continuous SER robustness. For addressing data sparsity, we investigate two methods that enable us to learn robust embeddings, which highlight the differences that exist between neutral speech and emotionally expressive speech, without requiring emotion annotations. In the first method, we demonstrate how emotionally charged vocal expressions change speaker characteristics as captured by embeddings extracted from a speaker identification model, and we propose the use of these embeddings in SER applications. In the second method, we propose a framework for learning emotion embeddings using audio-textual data that is not annotated for emotion. The unification of the methods and results presented in this thesis helps enable the development of more robust SER systems, making key advancements toward an interactive speech-based AI system that is capable of recognizing and interpreting human behaviors.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/166106/1/aldeneh_1.pd

An Ordinal Approach to Affective Computing

Both depression prediction and emotion recognition systems are often based on ordinal ground truth due to subjectively annotated datasets. Yet, both have so far been posed as classification or regression problems. These naive approaches have fundamental issues because they are not focused on ordering, unlike ordinal regression, which is the most appropriate for truly ordinal ground truth. Ordinal regression to date offers comparatively fewer, more limited methods when compared with other branches in machine learning, and its usage has been limited to specific research domains. Accordingly, this thesis presents investigations into ordinal approaches for affective computing by describing a consistent framework to understand all ordinal system designs, proposing ordinal systems for large datasets, and introducing tools and principles to select suitable system designs and evaluation methods. First, three learning approaches are compared using the support vector framework to establish the empirical advantages of ordinal regression, which is lacking from the current literature. Results on depression and emotion corpora indicate that ordinal regression with proper tuning can improve existing depression and emotion systems. Ordinal logistic regression (OLR), which is an extension of logistic regression for ordinal scales, contributes to a number of model structures, from which the best structure must be chosen. Exploiting the newly proposed computationally efficient greedy algorithm for model structure selection (GREP), OLR outperformed or was comparable with state-of-the-art depression systems on two benchmark depression speech datasets. Deep learning has dominated many affective computing fields, and hence ordinal deep learning is an attractive prospect. However, it is under-studied even in the machine learning literature, which motivates an in-depth analysis of appropriate network architectures and loss functions. One of the significant outcomes of this analysis is the introduction of RankCNet, a novel ordinal network which utilises a surrogate loss function of rank correlation. Not only the modelling algorithm but the choice of evaluation measure depends on the nature of the ground truth. Rank correlation measures, which are sensitive to ordering, are more apt for ordinal problems than common classification or regression measures that ignore ordering information. Although rank-based evaluation for ordinal problems is not new, so far in affective computing, ordinality of the ground truth has been widely ignored during evaluation. Hence, a systematic analysis in the affective computing context is presented, to provide clarity and encourage careful choice of evaluation measures. Another contribution is a neural network framework with a novel multi-term loss function to assess the ordinality of ordinally-annotated datasets, which can guide the selection of suitable learning and evaluation methods. Experiments on multiple synthetic and affective speech datasets reveal that the proposed system can offer reliable and meaningful predictions about the ordinality of a given dataset. Overall, the novel contributions and findings presented in this thesis not only improve prediction accuracy but also encourage future research towards ordinal affective computing: a different paradigm, but often the most appropriate