5 papers · 1 filter
SEER: The Span-based Emotion Evidence Retrieval Benchmark
Aneesha Sampath, Oya Aran, Emily Mower Provost
We introduce the SEER (Span-based Emotion Evidence Retrieval) Benchmark to test Large Language Models' (LLMs) ability to identify the specific spans of text that express emotion. U…
More Similar than Dissimilar: Modeling Annotators for Cross-Corpus Speech Emotion Recognition
James Tavernor, Emily Mower Provost
Speech emotion recognition systems often predict a consensus value generated from the ratings of multiple annotators. However, these models have limited ability to predict the anno…
The Whole Is Bigger Than the Sum of Its Parts: Modeling Individual Annotators to Capture Emotional Variability
James Tavernor, Yara El-Tawil, Emily Mower Provost
Emotion expression and perception are nuanced, complex, and highly subjective processes. When multiple annotators label emotional data, the resulting labels contain high variabilit…
Contrastive Distillation of Emotion Knowledge from LLMs for Zero-Shot Emotion Recognition
Minxue Niu, Emily Mower Provost
The ability to handle various emotion labels without dedicated training is crucial for building adaptable Emotion Recognition (ER) systems. Conventional ER models rely on training…
Efficient Finetuning for Dimensional Speech Emotion Recognition in the Age of Transformers
Aneesha Sampath, James Tavernor, Emily Mower Provost
Accurate speech emotion recognition is essential for developing human-facing systems. Recent advancements have included finetuning large, pretrained transformer models like Wav2Vec…