DementiaBank-Emotion: A Multi-Rater Emotion Annotation Corpus for Alzheimer's Disease Speech (Version 1.0)

TL;DR

This paper introduces DementiaBank-Emotion, a new multi-rater emotion annotation corpus for Alzheimer's speech, revealing differences in emotional expression and acoustic features between AD patients and healthy controls.

Contribution

It provides the first multi-rater emotion annotation dataset for AD speech, along with analysis of emotional expression differences and acoustic features in clinical populations.

Findings

AD patients express more non-neutral emotions than controls

Control speakers modulate F0 significantly for sadness, unlike AD speakers

Loudness within AD speech helps differentiate emotion categories

Abstract

We present DementiaBank-Emotion, the first multi-rater emotion annotation corpus for Alzheimer's disease (AD) speech. Annotating 1,492 utterances from 108 speakers for Ekman's six basic emotions and neutral, we find that AD patients express significantly more non-neutral emotions (16.9%) than healthy controls (5.7%; p < .001). Exploratory acoustic analysis suggests a possible dissociation: control speakers showed substantial F0 modulation for sadness (Delta = -3.45 semitones from baseline), whereas AD speakers showed minimal change (Delta = +0.11 semitones; interaction p = .023), though this finding is based on limited samples (sadness: n=5 control, n=15 AD) and requires replication. Within AD speech, loudness differentiates emotion categories, indicating partially preserved emotion-prosody mappings. We release the corpus, annotation guidelines, and calibration workshop materials to support research on emotion recognition in clinical populations.