Detecting Emotion in Speech: Validating a Remote Assessment Tool
Bibliographic record
Abstract
Recent global events have called for the development of online testing and evaluation techniques, for both clinical and research purposes. Online testing is especially important for older adults who might have limited access to health services due to mobility difficulties or health risks. The current study aimed to validate an online tool, Internet-based Test for Rating of Emotions in Speech (iT-RES), designed to test the perception of emotions in spoken language and assess its efficacy as a remote telehealth assessment tool for older adults. Forty-one older adults (age 60–80) and 44 young adults (age 20–29) performed the online iT-RES. Age-related differences in the online tool were compared with respective age-related effects in a parallel lab-based tool. The three main age-related effects found in the lab-based version were replicated in the iT-RES online version. (1) Better identification of emotion by young adults; (2) Failures of Selective Attention were larger for older adults; (3) Older adults gave lower weight to the prosodic channel as compared to younger adults. Our findings add to the growing body of literature regarding the validity of online neuropsychological assessment. We also present and discuss several measures taken to ensure quality online testing and increase overall test validity.
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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".