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

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.

Understanding Emotion in Discourse: Recognition Insights and Linguistic Patterns for Generation

While Emotion Recognition in Conversation (ERC) has achieved high accuracy, two critical gaps remain: a limited understanding of \textit{which} architectural choices actually matter, and a lack of linguistic analysis connecting recognition to generation. We address both gaps through a systematic analysis of the IEMOCAP dataset. For recognition, we conduct a rigorous ablation study with 10-seed evaluation and report three key findings. First, conversational context is paramount, with performance saturating rapidly -- 90\% of the total gain achieved within just the most recent 10--30 preceding turns (depending on the label set). Second, hierarchical sentence representations help at utterance-level, but this benefit disappears once conversational context is provided, suggesting that context subsumes intra-utterance structure. Third, external affective lexicons (SenticNet) provide no gain, indicating that pre-trained encoders already capture necessary emotional semantics. With simple architectures using strictly causal context, we achieve 82.69\% (4-way) and 67.07\% (6-way) weighted F1, outperforming prior text-only methods including those using bidirectional context. For linguistic analysis, we analyze 5,286 discourse marker occurrences and find a significant association between emotion and marker positioning ($p < .0001$). Notably, "sad" utterances exhibit reduced left-periphery marker usage (21.9\%) compared to other emotions (28--32\%), consistent with theories linking left-periphery markers to active discourse management. This connects to our recognition finding that sadness benefits most from context (+22\%p): lacking explicit pragmatic signals, sad utterances require conversational history for disambiguation.