We propose emotion2vec, a universal speech emotion representation model. emotion2vec is pre-trained on open-source unlabeled emotion data through self-supervised online distillation, combining utterance-level loss and frame-level loss during pre-training. emotion2vec outperforms state-of-the-art pre-trained universal models and emotion specialist models by only training linear layers for the speech emotion recognition task on the mainstream IEMOCAP dataset. In addition, emotion2vec shows consistent improvements among 10 different languages of speech emotion recognition datasets. emotion2vec also shows excellent results on other emotion tasks, such as song emotion recognition, emotion prediction in conversation, and sentiment analysis. Comparison experiments, ablation experiments, and visualization comprehensively demonstrate the universal capability of the proposed emotion2vec. To the best of our knowledge, emotion2vec is the first universal representation model in various emotion-related tasks, filling a gap in the field.

pdf/emotion2vec-self-supervised-pre-training-for-speech-emotion-3goykbfmdl.pdf

emotion2vec: Self-Supervised Pre-Training for Speech Emotion Representation

Loneliness, or perceived social isolation, is a leading predictor of all-cause mortality and is increasingly considered a public health epidemic afflicting significant portions of the general population. Chronic loneliness is itself associated with two of the most pressing public health epidemics currently facing the globe: the rise of mental illness and metabolic health disorders. Here, we highlight the epidemiological associations between loneliness and mental and metabolic health disorders and argue that loneliness contributes to the etiology of these conditions by acting as a chronic stressor that leads to neuroendocrine dysregulation and downstream immunometabolic consequences that manifest in disease. Specifically, we describe how loneliness can lead to overactivation of the hypothalamic-pituitary-adrenal axis and ultimately cause mitochondrial dysfunction, which is implicated in mental and metabolic disease. These conditions can, in turn, lead to further social isolation and propel a vicious cycle of chronic illness. Finally, we outline interventions and policy recommendations that can reduce loneliness at both the individual and community levels. Given its role in the etiology of the most prevalent chronic diseases of our time, focusing resources on alleviating loneliness is a vitally important and cost-effective public health strategy.

/pdf/breaking-the-vicious-cycle-the-interplay-between-loneliness-2llujouu.pdf

Breaking the vicious cycle: The interplay between loneliness, metabolic illness, and mental health

Recent years have witnessed the intensive development of live streaming E-commerce, an emerging business mode. Although it contributes to economic growth, various forms of chaos show up and disturbs the market order. Therefore, from 1 July 2020, the official release of the first domestic document on live streaming E-commerce, the Code of Conduct for Online Live Streaming Marketing, to the end of the first half of 2021, China has witnessed so intensive release of relevant policies that are rare over the past years. Introducing these policies will inevitably attract the general public’s attention and discussions. Based on online comments, this paper uses the LDA models to extract topics from online comments related to live streaming E-commerce and identifies sentiment polarity and sentiment intensity by the analysis models of different emotion dictionaries to study policy implementation effects and the main topics of concern before and after the policy implementation. The analysis results show that people between the age of 20 and 40 attach more importance to the implementation of the normative policy for live streaming E-commerce. Women, the main force of live streaming users, are less enthusiastic about the policy implementation than men. Moreover, the analysis results of the LDA models and online HDP (online hierarchical Dirichlet process) models demonstrate that the most discussed topics are the contribution of live streaming E-commerce to traditional economic transformation, public welfare activities, resumption of work and production, and poverty alleviation, as well as fraud, counterfeit goods, supervision, rights protection and other incidents in this industry. Overall, the majority of the public holds a positive attitude towards the policy implementation. After further analysis of comments under the relevant topics, it is found that compared with the first two policies released on 1 July and 5 November in 2020, although the proportion of netizens with positive emotions during the implementation of the follow-up policy has increased, the increment is not significant, indicating that the implementation of the new normative policy in a short term will hardly curb the occurrence of industry chaos. In turn, the governments should transfer their attention to actual regulatory problems, and intensify efforts to implement normative policies.

/pdf/analysis-of-the-public-opinion-evolution-on-the-normative-2zq80h7f.pdf

Analysis of the Public Opinion Evolution on the Normative Policies for the Live Streaming E-Commerce Industry Based on Online Comment Mining under COVID-19 Epidemic in China

To support applications of speech-driven interactive systems in various conversational scenarios, text-to-speech (TTS) synthesis needs to understand the conversational context and determine appropriate speaking styles in its synthesized speeches. These speaking styles are influenced by the dependencies between the multi-modal information in the context at both global scale (i.e. utterance level) and local scale (i.e. word level). However, the dependency modeling and speaking style inference at the local scale are largely missing in state-of-the-art TTS systems, resulting in the synthesis of incorrect or improper speaking styles. In this paper, to learn the dependencies in conversations at both global and local scales and to improve the synthesis of speaking styles, we propose a context modeling method which models the dependencies among the multi-modal information in context with multi-scale relational graph convolutional network (MSRGCN). The learnt multi-modal context information at multiple scales is then utilized to infer the global and local speaking styles of the current utterance for speech synthesis. Experiments demonstrate the effectiveness of the proposed approach, and ablation studies reflect the contributions from modeling multi-modal information and multi-scale dependencies.

/pdf/inferring-speaking-styles-from-multi-modal-conversational-ix0s6l3p.pdf

Inferring Speaking Styles from Multi-modal Conversational Context by Multi-scale Relational Graph Convolutional Networks

The aim of emotion prediction in conversation (EPC) is to predict the future emotional state of a speaker based on context information, which is essential for conducting a friendly human-computer conversation. Most EPC works only investigated context information by merging a speaker’s multiple utterances into a single utterance per turn and focused on conversations in a dual-speaker scenario, which ignored the information in multi-utterance turn and a more complex and natural scenario of multi-speaker conversations. This paper introduces a context information modeling approach that considers potential emotional interactive information within a speaker’s multi-utterance turn, which dominates his/her future emotions. Moreover, our approach advances emotion prediction in both dual-and multi-speaker conversations. Experimental results show that such an approach significantly enhances context information modeling and renders a higher accuracy in EPC than reported in the literature.

Emotion Awareness in Multi-utterance Turn for Improving Emotion Prediction in Multi-Speaker Conversation

Nowadays, depression has been a prominent mental health problem throughout the world. A common but negative social experience, social exclusion (also known as ostracism) is a great risk factor for individuals' health and adaptation. Undergraduate students are in a development period of challenges and transitions, so they are vulnerable to suffering from depression and negative social experiences. Against this background, the present study aimed to examine the association between social exclusion and undergraduate students' depression as well as the underlying mechanism - the mediating roles of rejection sensitivity and social self-efficacy. Seven hundred sixty-two undergraduate students were recruited to participate in this study, who were asked to complete a set of questionnaires measuring social exclusion, depression, rejection sensitivity, and social self-efficacy. After controlling for gender, social exclusion was positively associated with undergraduate students' depression. And rejection sensitivity and social self-efficacy could significantly mediate this relation through three mediating paths - the separate mediating effects of rejection sensitivity and social self-efficacy, as well as the serial mediating effect of rejection sensitivity and social self-efficacy. These results could not only deepen our understanding of this theme, but also have several practical implications for the intervention of depression, for example, relevant social skill training and cognitive therapy could be adopted to intervene the rejection sensitivity and social self-efficacy. 

/pdf/social-exclusion-and-depression-among-undergraduate-students-2nm2o8q4.pdf

Social Exclusion and Depression among undergraduate students: the mediating roles of rejection sensitivity and social self-efficacy

Can smartphone presence affect cognitive function? The moderating role of fear of missing out

Emotion prediction in conversation is important for humans to conduct a fluent conversation, which is an underexplored research topic in the affective computing area. In previous studies, predicting the coming emotion only considered the context information from one single speaker. However, there are two sides of the speaker and listener in interlocutors, and their emotions are influenced by one another during the conversation. For this reason, we propose a dimensional emotion prediction model based on interactive information in conversation from both interlocutors. We investigate the effects of interactive information in four conversation situations on emotion prediction, in which emotional tendencies of interlocutors are consistent or inconsistent in both valence and arousal. The results showed that the proposed method performance better by considering the interactive context information than the ones considering one single side alone. The prediction result is affected by the conversation situations. In the situation interlocutors have consistent emotional tendency in valence and inconsistent tendency in arousal, the prediction performance of valence is the best. In the situation that interlocutors’ emotional tendency is inconsistent in both valence and arousal, the prediction performance of arousal is the best.

Dimensional Emotion Prediction Based on Interactive Context in Conversation.

Background: During the COVID-19 pandemic, depressive symptoms, a common emotional problem among adolescents, have become more prominent. Regarding the influencing factors of adolescent depressive symptoms, it is widely accepted that parents’ problematic cellphone use around the family (specifically parental phubbing) is a strong predictive factor for the development of depressive symptoms among adolescents. Notably, the COVID-19 pandemic caused a sharp increase in the number of individuals with depressive symptoms, and the negative consequences of parental phubbing and depressive symptoms might have been exacerbated. Accordingly, this study aimed to examine the association between parental phubbing and adolescent depressive symptoms as well as their underlying mechanism. Method: To test our hypotheses, we conducted an offline/online survey with 614 adolescents in Central China from May to June 2022, which corresponded to a period of strict lockdowns in some areas due to the outbreak of the Omicron variant. The participants completed a set of measures, including a technology interference questionnaire, a parent–child relationship scale, a self-concept clarity scale, and the depressive symptoms scale. Results: Parental phubbing was positively associated with adolescent depressive symptoms; the parent–child relationship and self-concept clarity could independently mediate this relationship; and the parent–child relationship and self-concept clarity were also serial mediators in this association. These findings extend previous research by highlighting the impact of parental technology use on their children and the underlying mechanism explaining adolescent depressive symptoms. They provide practical recommendations for parents to prioritize fostering a positive family environment and minimizing phubbing behaviors to enhance adolescent development, particularly in the context of the COVID-19 pandemic.

/pdf/parental-phubbing-and-adolescent-depressive-symptoms-during-24mtchem.pdf

Parental Phubbing and Adolescent Depressive Symptoms during COVID-19: A Serial Meditating Model

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