Estimating Public Speaking Anxiety from Speech Signals Using Unsupervised Transfer Learning Public speaking anxiety (PSA) ranks as a top social phobia across the world caused by various confounding factors. Motivated by the inherent data sparsity and lack of annotations in humanrelated applications, we propose unsupervised learning techniques to estimate PSA from speech signals. The labeled source data come from the publicly available CREMAD dataset, while the unlabeled target data come from reallife public speaking tasks. Since fear is one of the major factors of PSA, the goal of this study is to build fearspecific representations from the labeled source data to estimate the degree of fear in the target data, and examine the extent to which the latter is associated with anxiety during the public speaking encounter. Transfer learning is performed through the domainadversarial neural network (DANN) and Wasserstein generative adversarial network (WGAN). Results indicate that the proposed unsupervised fear specific estimates can detect public speaking anxiety with Pearsonxe2x80x99s correlation coefficient of 0.28 (p u003c0.01). When these fear specific estimates are combined with the degree of an individualxe2x80x99s preparation for the public speaking task, obtained through selfreports, they yield Pearsonxe2x80x99s correlation of 0.55 (p u003c0.01). These indicate the feasibility of leveraging labeled emotionspecific corpora for detecting humanrelated outcomes in reallife and provides a foundation for smart assistive technologies through the automated realtime estimation of anxiety during public speaking.

Different Performances of Speech and Natural Gait in Identifying Anxiety and Depression. Recognition of individualxe2x80x99s emotions using the data of speech and natural gait has been proved effective by previous studies. This study establishes a supervised regression model based on individualxe2x80x99s speech and natural gait data to predict the scores of depression and anxiety. The results show that the performance of the predictive model based on gait features is better than that of speech features. The basic processes are as follows: First, we recruited 88 participants and collected their speech data and natural gait data in laboratory; Second, we required each participant to finish the anxiety and depression scales (PHQ9, CESD, TAI, and GAD7), to get the anxiety and depression scores as the tagging data for modeling; Third, we extracted 6125 features from each participantxe2x80x99s speech data and 1602 features from the gait data; Finally, we used a variety of machine learning algorithms to train and test the predictive models. The results show that the max Pearson Correlation Coefficient between the predicted scores and the questionnaire scores of the speechbased model is 0.43, and the max Pearson Correlation Coefficient of the gaitbased model is 0.73, which is much better than the speechbased model.