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Public speaking is a vital skill for making good impressions, effectively exchanging ideas, and influencing others. Yet, public speaking anxiety (PSA) ranks as a top social phobia. Recent advancements in wearable devices and ubiquitous virtual reality (VR) interfaces can help measure and mitigate PSA. This research quantifies PSA through bio-behavioral markers related to individuals' physiological and acoustic characteristics. The effect of virtual reality (VR) training on alleviating PSA is measured through self-reported and bio-behavioral indices. Psychological (e.g., general trait anxiety, personality) and demographic (e.g., age, gender, highest education, native language) traits are examined as moderating factors between bio-behavioral indices and PSA, as well as moderating factors for measuring the VR effectiveness in mitigating PSA. These measures are also used as clustering criteria for stratifying participants in group-based models of PSA. Results indicate the significance of such traits to modeling PSA with the proposed group-based models yielding Spearman's correlation of 0:55 ( $p \lt 0:05$ ) between the actual and predicted outcome. Results further demonstrate that systematic exposure to public speaking in VR can alleviate PSA in terms of both self-reported ( $p \lt 0:05$ ) and physiological ( $p \lt 0:05$ ) indices. Findings from this study will enable researchers to better understand antecedents and causes of PSA and lay the foundation for personalized adaptive feedback for PSA interventions.

By monitoring human behavior unobtrusively, mobile sensing technologies have the potential to improve our daily lives. Initial results from a field study demonstrate that such passive technologies can detect a complex psychological state in an uncontrolled, real-life environment. In the web extra at https://youtu.be/n8Ap3Z44ojQ, guest editor Katarzyna Wac interviews authors Adela Timmons and Theodora Chaspari, quality-of-life technology researchers at the University of Southern California.

Speech-based machine learning (ML) has been heralded as a promising solution for tracking prosodic and spectrotemporal patterns in real-life that are indicative of emotional changes, providing a valuable window into one's cognitive and mental state. Yet, the scarcity of labelled data in ambulatory studies prevents the reliable training of ML models, which usually rely on "data-hungry" distribution-based learning. Leveraging the abundance of labelled speech data from acted emotions, this paper proposes a few-shot learning approach for automatically recognizing emotion in spontaneous speech from a small number of labelled samples. Few-shot learning is implemented via a metric learning approach through a siamese neural network, which models the relative distance between samples rather than relying on learning absolute patterns of the corresponding distributions of each emotion. Results indicate the feasibility of the proposed metric learning in recognizing emotions from spontaneous speech in four datasets, even with a small amount of labelled samples. They further demonstrate superior performance of the proposed metric learning compared to commonly used adaptation methods, including network fine-tuning and adversarial learning. Findings from this work provide a foundation for the ambulatory tracking of human emotion in spontaneous speech contributing to the real-life assessment of mental health degradation., IEEE Transactions on Affective Computing, early access article. doi: 10.1109/TAFFC.2021.3109485

Parametric dictionaries can increase the ability of sparse representations to meaningfully capture and interpret the underlying signal information, such as encountered in biomedical problems. Given a mapping function from the atom parameter space to the actual atoms, we propose a sparse Bayesian framework for learning the atom parameters, because of its ability to provide full posterior estimates, take uncertainty into account and generalize on unseen data. Inference is performed with Markov Chain Monte Carlo, that uses block sampling to generate the variables of the Bayesian problem. Since the parameterization of dictionary atoms results in posteriors that cannot be analytically computed, we use a Metropolis–Hastings–within-Gibbs framework, according to which variables with closed-form posteriors are generated with the Gibbs sampler, while the remaining ones with the Metropolis Hastings from appropriate candidate-generating densities. We further show that the corresponding Markov Chain is uniformly ergodic ensuring its convergence to a stationary distribution independently of the initial state. Results on synthetic data and real biomedical signals indicate that our approach offers advantages in terms of signal reconstruction compared to previously proposed Steepest Descent and Equiangular Tight Frame methods. This paper demonstrates the ability of Bayesian learning to generate parametric dictionaries that can reliably represent the exemplar data and provides the foundation towards inferring the entire variable set of the sparse approximation problem for signal denoising, adaptation, and other applications.

Biometric sensors and portable devices are being increasingly embedded into our everyday life, creating the need for robust physiological models that efficiently represent, analyze, and interpret the acquired signals. We propose a knowledge-driven method to represent electrodermal activity (EDA), a psychophysiological signal linked to stress, affect, and cognitive processing. We build EDA-specific dictionaries that accurately model both the slow varying tonic part and the signal fluctuations, called skin conductance responses (SCR), and use greedy sparse representation techniques to decompose the signal into a small number of atoms from the dictionary. Quantitative evaluation of our method considers signal reconstruction, compression rate, and information retrieval measures, that capture the ability of the model to incorporate the main signal characteristics, such as SCR occurrences. Compared to previous studies fitting a predetermined structure to the signal, results indicate that our approach provides benefits across all aforementioned criteria. This paper demonstrates the ability of appropriate dictionaries along with sparse decomposition methods to reliably represent EDA signals and provides a foundation for automatic measurement of SCR characteristics and the extraction of meaningful EDA features.

Human behavior offers a window into the mind. When we observe someone's actions, we are constantly inferring his or her mental states-their beliefs, intents, and knowledge-a concept known as theory of mind. For example.