(Not) hearing happiness: Predicting fluctuations in happy mood from acoustic cues using machine learning.
Bibliographic record
Abstract
Recent popular claims surrounding virtual assistants suggest that computers will soon be able to hear our emotions. Supporting this possibility, promising work has harnessed big data and emergent technologies to automatically predict stable levels of one specific emotion, happiness, at the community (e.g., counties) and trait (i.e., people) levels. Furthermore, research in affective science has shown that nonverbal vocal bursts (e.g., sighs, gasps) and specific acoustic features (e.g., pitch, energy) can differentiate between distinct emotions (e.g., anger, happiness) and that machine-learning algorithms can detect these differences. Yet, to our knowledge, no work has tested whether computers can automatically detect normal, everyday, within-person fluctuations in one emotional state from acoustic analysis. To address this issue in the context of happy mood, across 3 studies (total N = 20,197), we asked participants to repeatedly report their state happy mood and to provide audio recordings-including both direct speech and ambient sounds-from which we extracted acoustic features. Using three different machine learning algorithms (neural networks, random forests, and support vector machines) and two sets of acoustic features, we found that acoustic features yielded minimal predictive insight into happy mood above chance. Neither multilevel modeling analyses nor human coders provided additional insight into state happy mood. These findings suggest that it is not yet possible to automatically assess fluctuations in one emotional state (i.e., happy mood) from acoustic analysis, pointing to a critical future direction for affective scientists interested in acoustic analysis of emotion and automated emotion detection. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".