71 citations · 343 across the 43 of their papers we have counts for
11 papers · 1 filter
Identifying depression-related topics in smartphone-collected free-response speech recordings using an automatic speech recognition system and a deep learning topic model
Yuezhou Zhang, Amos A Folarin, Judith Dineley +25
Language use has been shown to correlate with depression, but large-scale validation is needed. Traditional methods like clinic studies are expensive. So, natural language processi…
Refashioning Emotion Recognition Modelling: The Advent of Generalised Large Models
Zixing Zhang, Liyizhe Peng, Tao Pang +3
After the inception of emotion recognition or affective computing, it has increasingly become an active research topic due to its broad applications. Over the past couple of decade…
Can ChatGPT's Responses Boost Traditional Natural Language Processing?
Mostafa M. Amin, Erik Cambria, Björn W. Schuller
The employment of foundation models is steadily expanding, especially with the launch of ChatGPT and the release of other foundation models. These models have shown the potential o…
MER 2023: Multi-label Learning, Modality Robustness, and Semi-Supervised Learning
Zheng Lian, Haiyang Sun, Licai Sun +15
The first Multimodal Emotion Recognition Challenge (MER 2023) was successfully held at ACM Multimedia. The challenge focuses on system robustness and consists of three distinct tra…
Will Affective Computing Emerge from Foundation Models and General AI? A First Evaluation on ChatGPT
Mostafa M. Amin, Erik Cambria, Björn W. Schuller
ChatGPT has shown the potential of emerging general artificial intelligence capabilities, as it has demonstrated competent performance across many natural language processing tasks…
MuSe-Toolbox: The Multimodal Sentiment Analysis Continuous Annotation Fusion and Discrete Class Transformation Toolbox
Lukas Stappen, Lea Schumann, Benjamin Sertolli +4
We introduce the MuSe-Toolbox - a Python-based open-source toolkit for creating a variety of continuous and discrete emotion gold standards. In a single framework, we unify a wide…