Paralinguistics, the non-lexical components of speech, play a crucial role in human-human interaction. Models designed to recognize paralinguistic information, particularly speech emotion and style, are difficult to train because of the limited labeled datasets available. In this work, we present a new framework that enables a neural network to learn to extract paralinguistic attributes from speech using data that are not annotated for emotion. We assess the utility of the learned embeddings on the downstream tasks of emotion recognition and speaking style detection, demonstrating significant improvements over surface acoustic features as well as over embeddings extracted from other unsupervised approaches. Our work enables future systems to leverage the learned embedding extractor as a separate component capable of highlighting the paralinguistic components of speech.

The COVID-19 pandemic, like many of the disease outbreaks that have preceded it, is likely to have a profound effect on mental health. Understanding its impact can inform strategies for mitigating negative consequences. In this work, we seek to better understand the effects of COVID-19 on mental health by examining discussions within mental health support communities on Reddit. First, we quantify the rate at which COVID-19 is discussed in each community, or subreddit, in order to understand levels of pandemic-related discussion. Next, we examine the volume of activity in order to determine whether the number of people discussing mental health has risen. Finally, we analyze how COVID-19 has influenced language use and topics of discussion within each subreddit.

Endowing automated agents with the ability to provide support, entertainment and interaction with human beings requires sensing of the users’ affective state. These affective states are impacted by a combination of emotion inducers, current psychological state, and various conversational factors. Although emotion classification in both singular and dyadic settings is an established area, the effects of these additional factors on the production and perception of emotion is understudied. This paper presents a new dataset, Multimodal Stressed Emotion (MuSE), to study the multimodal interplay between the presence of stress and expressions of affect. We describe the data collection protocol, the possible areas of use, and the annotations for the emotional content of the recordings. The paper also presents several baselines to measure the performance of multimodal features for emotion and stress classification.