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Found 13 papers, 1 papers with code
From Text to Emotion: Unveiling the Emotion Annotation Capabilities of LLMs
Training emotion recognition models has relied heavily on human annotated data, which present diversity, quality, and cost challenges.
The Future of Open Human Feedback
In this work, we bring together interdisciplinary experts to assess the opportunities and challenges to realizing an open ecosystem of human feedback for AI.
Lessons from the Trenches on Reproducible Evaluation of Language Models
Third, we present the Language Model Evaluation Harness (lm-eval): an open source library for independent, reproducible, and extensible evaluation of language models that seeks to address these issues.
Implicit Design Choices and Their Impact on Emotion Recognition Model Development and Evaluation
This research advances robust, practical emotion recognition through multifaceted studies of challenges in datasets, labels, modeling, demographic and membership variable encoding in representations, and evaluation.
Best Practices for Noise-Based Augmentation to Improve the Performance of Deployable Speech-Based Emotion Recognition Systems
We end the paper with a set of recommendations for noise augmentations in speech emotion recognition datasets.
Human-Imitating Metrics for Training and Evaluating Privacy Preserving Emotion Recognition Models Using Sociolinguistic Knowledge
In this paper, we propose an automatic and quantifiable metric that allows us to evaluate humans' perception of a model's ability to preserve privacy with respect to sensitive variables.
MuSE: a Multimodal Dataset of Stressed Emotion
Endowing automated agents with the ability to provide support, entertainment and interaction with human beings requires sensing of the users{'} affective state.
Privacy Enhanced Multimodal Neural Representations for Emotion Recognition
In this work, we show how multimodal representations trained for a primary task, here emotion recognition, can unintentionally leak demographic information, which could override a selected opt-out option by the user.
Identifying Mood Episodes Using Dialogue Features from Clinical Interviews
Bipolar disorder, a severe chronic mental illness characterized by pathological mood swings from depression to mania, requires ongoing symptom severity tracking to both guide and measure treatments that are critical for maintaining long-term health.
Controlling for Confounders in Multimodal Emotion Classification via Adversarial Learning
Our results show that stress is indeed encoded in trained emotion classifiers and that this encoding varies across levels of emotions and across the lexical and acoustic modalities.
MuSE-ing on the Impact of Utterance Ordering On Crowdsourced Emotion Annotations
As a result, annotations are colored by the manner in which they were collected.
"Hang in There": Lexical and Visual Analysis to Identify Posts Warranting Empathetic Responses
In the past few years, social media has risen as a platform where people express and share personal incidences about abuse, violence and mental health issues.
The Truth and Nothing but the Truth: Multimodal Analysis for Deception Detection
We propose a data-driven method for automatic deception detection in real-life trial data using visual and verbal cues.
