The first LLMs for voice and emotional intelligence

What emotions do the face and body express? Guided by new conceptual and quantitative approaches (Cowen, Elfenbein, Laukka, & Keltner, 2018; Cowen & Keltner, 2017, 2018), we explore the taxonomy of emotion recognized in facial-bodily expression.

Emotional vocalizations are central to human social life. Recent studies have documented that people recognize at least 13 emotions in brief vocalizations. This capacity emerges early in development, is preserved in some form across cultures, and informs how people respond emotionally to music.

Claims about how reported emotional experiences are geometrically organized within a semantic space have shaped the study of emotion. Using statistical methods to analyze reports of emotional states elicited by 2,185 emotionally evocative short videos with richly varying situational content, we uncovered 27 varieties of reported emotional experience.

Central to the study of emotion is evidence concerning its universality, particularly the degree to which emotional expressions are similar across cultures. Here, we present an approach to studying the universality of emotional expression that rules out cultural contact and circumvents potential biases in survey-based methods: A computational analysis of apparent facial expressions portrayed in artwork created by members of cultures isolated from Western civilization.

What would a comprehensive atlas of human emotions include? For 50 years, scientists have sought to map emotion-related experience, expression, physiology, and recognition in terms of the “basic six”—anger, disgust, fear, happiness, sadness, and surprise.

Emotional vocalizations are central to human social life. Recent studies have documented that people recognize at least 13 emotions in brief vocalizations. This capacity emerges early in development, is preserved in some form across cultures, and informs how people respond emotionally to music.

Understanding emotion expressed in language has a wide range of applications, from building empathetic chatbots to detecting harmful online behavior. Advancement in this area can be improved using large-scale datasets with a fine-grained typology, adaptable to multiple downstream tasks.

Central to science and technology are questions about how to measure facial expression. Thecurrent gold standard is the facial action coding system (FACS), which is often assumed toaccount for all facial muscle movements relevant toperceived emotion. However, the mapping from FACS codes to perceived emotion is not well understood.

What would a comprehensive atlas of human emotions include? For 50 years, scientists have sought to map emotion-related experience, expression, physiology, and recognition in terms of the “basic six”—anger, disgust, fear, happiness, sadness, and surprise.

Core to understanding emotion are subjective experiences and their expression in facial behavior. Past studies have largely focused on six emotions and prototypical facial poses, reflecting limitations in scale and narrow assumptions about the variety of emotions and their patterns of expression.

Here we present semantic space theory and the data-driven methods it entails. Across the largest studies to date of emotion-related experience, expression, and physiology, we find that emotion is high dimensional, defined by blends of upward of 20 distinct kinds of emotions, and not reducible to low-dimensional structures and conceptual processes as assumed by constructivist accounts.

Human social life is rich with sighs, chuckles, shrieks and other emotional vocalizations, called ‘vocal bursts’. Nevertheless, the meaning of vocal bursts across cultures is only beginning to be understood. Here, we combined large-scale experimental data collection with deep learning to reveal the shared and culture-specific meanings of vocal bursts.

The 3rd Multimodal Sentiment Analysis Challenge (MuSe) focuses on multimodal affective computing.

Social Functionalist Theory (SFT) emerged 20 years ago to orient emotion science to the social nature of emotion. Here we expand upon SFT and make the case for how emotions, relationships, and culture constitute one another.

Emotional expressions are a language of social interaction. Guided by recent advances in the study of expression and intersectionality, the present investigation examined how gender, ethnicity, and social class influence the signaling and recognition of 34 states in dynamic full-body expressive behavior

Emotional vocalizations are central to human social life. Recent studies have documented that people recognize at least 13 emotions in brief vocalizations. This capacity emerges early in development, is preserved in some form across cultures, and informs how people respond emotionally to music.

What emotions do the face and body express? Guided by new conceptual and quantitative approaches, we explore the taxonomy of emotion recognized in facial-bodily expression. Participants judged the emotions captured in 1,500 photographs of facial-bodily expression in terms of emotion categories, appraisals, free response, and ecological validity.

The ICML Expressive Vocalization (EXVO) Competition is focused on understanding and generating vocal bursts: laughs, gasps, cries, and other non-verbal vocalizations that are central to emotional expression and communication.

The ACII Affective Vocal Bursts Workshop & Competition is focused on understanding multiple affective dimensions of vocal bursts: laughs, gasps, cries, screams, and many other non-linguistic vocalizations central to the expression of emotion and to human communication more generally.

In this article, we review recent developments in the study of emotional expression within a basic emotion framework. Dozens of new studies find that upwards of 20 emotions are signaled in multimodal and dynamic patterns of expressive behavior.

Cross-cultural studies of the meaning of facial expressions have largely focused on judgments of small sets of stereotypical images by small numbers of people. Here, we used large-scale data collection and machine learning to map what facial expressions convey in six countries.