4 papers
What You Feel Is Not What They See: On Predicting Self-Reported Emotion from Third-Party Observer Labels
Yara El-Tawil, Aneesha Sampath, Emily Mower Provost
Self-reported emotion labels capture internal experience, while third-party labels reflect external perception. These perspectives often diverge, limiting the applicability of thir…
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…
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…
Beyond Binary: Multiclass Paraphasia Detection with Generative Pretrained Transformers and End-to-End Models
Matthew Perez, Aneesha Sampath, Minxue Niu +1
Aphasia is a language disorder that can lead to speech errors known as paraphasias, which involve the misuse, substitution, or invention of words. Automatic paraphasia detection ca…