Impaired Prosodic Processing but Not Hearing Function Is Associated with an Age-Related Reduction in AI Speech Recognition
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: Voice artificial intelligence (AI) technology is becoming increasingly common. Recent work indicates that middle-aged to older adults are less able to identify modern AI speech compared to younger adults, but the underlying causes are unclear. METHODS: The current study with younger and middle-aged to older adults investigated factors that could explain the age-related reduction in AI speech identification. Experiment 1 investigated whether high-frequency information in speech-to which middle-aged to older adults often have less access due sensitivity loss at high frequencies-contributes to age-group differences. Experiment 2 investigated whether an age-related reduction in the ability to process prosodic information in speech predicts the reduction in AI speech identification. RESULTS: Results for Experiment 1 show that middle-aged to older adults are less able to identify AI speech for both full-bandwidth speech and speech for which information above 4 kHz is removed, making the contribution of high-frequency hearing loss unlikely. Experiment 2 shows that the ability to identify AI speech is greater in individuals who also show a greater ability to identify emotions from prosodic speech information, after accounting for hearing function and self-rated experience with voice-AI systems. CONCLUSIONS: The current results suggest that the ability to identify AI speech is related to the accurate processing of prosodic information.
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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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".