@inproceedings{jeong-etal-2026-dementiabank,
title = "{D}ementia{B}ank-Emotion: A Multi-Rater Emotion Annotation Corpus for {A}lzheimer{'}s Disease Speech (Version 1.0)",
author = "Jeong, Cheonkam and
Liao, Jessica and
Lu, Audrey and
Song, Yutong and
Rashidian, Christopher and
Krogh, Donna and
Krogh, Erik and
Rasouli, Mahkameh and
Lee, Jung-Ah and
Dutt, Nikil and
Gibbs, Lisa M and
Sultzer, David and
Rousseau, Julie and
Ludlow, Jocelyn and
Galvez, Margaret and
Nuth, Alexander and
Khay, Chet and
Brunswicker, Sabine and
Nyamathi, Adeline",
editor = {Danilova, Vera and
Kurfal{\i}, Murathan and
S{\"o}derfeldt, Ylva and
Reed, Julia and
Burchell, Andrew},
booktitle = "Proceedings of the 1st Workshop on Linguistic Analysis for Health ({H}ea{L}ing 2026)",
month = mar,
year = "2026",
address = "Rabat, Morocco",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.healing-1.28/",
pages = "314--337",
ISBN = "979-8-89176-367-8",
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 {\ensuremath{<}} .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."
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<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 \ensuremath< .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.</abstract>
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%0 Conference Proceedings
%T DementiaBank-Emotion: A Multi-Rater Emotion Annotation Corpus for Alzheimer’s Disease Speech (Version 1.0)
%A Jeong, Cheonkam
%A Liao, Jessica
%A Lu, Audrey
%A Song, Yutong
%A Rashidian, Christopher
%A Krogh, Donna
%A Krogh, Erik
%A Rasouli, Mahkameh
%A Lee, Jung-Ah
%A Dutt, Nikil
%A Gibbs, Lisa M.
%A Sultzer, David
%A Rousseau, Julie
%A Ludlow, Jocelyn
%A Galvez, Margaret
%A Nuth, Alexander
%A Khay, Chet
%A Brunswicker, Sabine
%A Nyamathi, Adeline
%Y Danilova, Vera
%Y Kurfalı, Murathan
%Y Söderfeldt, Ylva
%Y Reed, Julia
%Y Burchell, Andrew
%S Proceedings of the 1st Workshop on Linguistic Analysis for Health (HeaLing 2026)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-367-8
%F jeong-etal-2026-dementiabank
%X 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 \ensuremath< .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.
%U https://aclanthology.org/2026.healing-1.28/
%P 314-337
Markdown (Informal)
[DementiaBank-Emotion: A Multi-Rater Emotion Annotation Corpus for Alzheimer’s Disease Speech (Version 1.0)](https://aclanthology.org/2026.healing-1.28/) (Jeong et al., HeaLing 2026)
ACL
- Cheonkam Jeong, Jessica Liao, Audrey Lu, Yutong Song, Christopher Rashidian, Donna Krogh, Erik Krogh, Mahkameh Rasouli, Jung-Ah Lee, Nikil Dutt, Lisa M Gibbs, David Sultzer, Julie Rousseau, Jocelyn Ludlow, Margaret Galvez, Alexander Nuth, Chet Khay, Sabine Brunswicker, and Adeline Nyamathi. 2026. DementiaBank-Emotion: A Multi-Rater Emotion Annotation Corpus for Alzheimer’s Disease Speech (Version 1.0). In Proceedings of the 1st Workshop on Linguistic Analysis for Health (HeaLing 2026), pages 314–337, Rabat, Morocco. Association for Computational Linguistics.