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Predicting Latent Narrative Mood Using Audio and Physiologic Data
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use neural network with optimized topology instead of multinomial logistic regression | segment-level classifier · Segment-level emotion | accuracy (%): 40.8 -> 45.3 | Helped |
| optimize location of each selected feature within network layers (ablation) | segment-level NN · Segment-level emotion | accuracy (%): 45.3 -> 47.3 | Helped |
| adjust predictions with state transition probabilities (Markov assumption) (ablation) | segment-level NN · Segment-level emotion | accuracy | Hurt |
| use logistic regression NN (0 hidden layers) instead of next best classifier | narrative-level classifier · Narrative-level happy/sad | AUC (mean): 0.90 -> 0.92 | Helped |
Closest papers by meaning
- EEVR: A Dataset of Paired Physiological Signals and Textual Descriptions for Joint Emotion Representation Learning (NeurIPS 2024 · similarity 0.85)
- FEEL: Quantifying Heterogeneity in Physiological Signals for Generalizable Emotion Recognition (NeurIPS 2025 · similarity 0.83)
- VowelPrompt: Hearing Speech Emotions from Text via Vowel-level Prosodic Augmentation (ICLR 2026 · similarity 0.81)
- Enriching Multimodal Sentiment Analysis Through Textual Emotional Descriptions of Visual-Audio Content (AAAI 2025 · similarity 0.81)
- Dynamic-SUPERB Phase-2: A Collaboratively Expanding Benchmark for Measuring the Capabilities of Spoken Language Models with 180 Tasks (ICLR 2025 · similarity 0.81)
- OV-MER: Towards Open-Vocabulary Multimodal Emotion Recognition (ICML 2025 · similarity 0.81)
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