Impaired prosodic processing but not hearing function is associated with reduced recognition of AI speech in older adults
Bibliographic record
Abstract
Voice artificial intelligence (AI) technology becomes increasingly common in everyday life, for example, in automated phone services, voice assistive systems (e.g., Siri), and social chat bots. However, most research has focused on how younger adults perceive modern AI speech, leaving the development of this technology age-uninformed. Recent work indicates that older adults are less able to identify modern AI speech compared to younger adults, but the underlying causes are unclear. The current study with younger (N=133; 22-39 years) and older adults (N=146; 54-79 years) investigated potential factors that could explain the age-related reduction in AI speech identification. In Experiment 1, we investigated whether high-frequency information in speech – to which older adults have less access due to hearing loss – contributes to age-group differences, but our results showed that older adults were less able to identify AI speech for both full-bandwidth speech and speech for which information above 4 kHz was removed. This result makes the contribution of hearing loss less likely. In Experiment 2, we investigated whether the known age-related reduction in the ability to process prosodic information in speech predicts the reduction in AI speech identification. Indeed, the ability to identify AI speech was greater in individuals who also showed a greater ability to identify emotions from prosodic speech information, after accounting for hearing function and self-rated experience with voice AI systems. The current results suggest that the ability to identify AI speech is related to the accurate processing of prosodic information.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.003 | 0.000 |
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".