The perception of artificial-intelligence (AI) based synthesized speech in younger and older adults
Bibliographic record
Abstract
Artificial intelligence (AI) based synthesized speech has become almost human-like, ubiquitous in everyday live (e.g., smart phones, grocery self-checkouts), and relatively easy to synthesize. This opens opportunities to use AI speech in research and clinical areas, such as hearing sciences, audiology, and speech pathology, where recordings of speech materials by voice actors can be time- and cost-intensive. However, much research thus far has focused on technological developments towards more human-like voices evaluated by younger adults. How older adults perceive AI speech is unclear. Using Google’s Wavenet text-to-speech synthesizer, the current study explores whether AI speech can be used to investigate common speech-in-noise perception phenomena in younger and older adults. Speech intelligibility was recorded for human speech and synthesized speech masked by a modulated or an unmodulated multi-talker babble noise. For both human and AI speech, speech intelligibility was better for the modulated than the unmodulated masker (masking release), and this masking-release benefit was reduced in older adults. Release from masking effects were comparable between human and AI speech, suggesting that modern AI speech could be useful for hearing and speech research. The data further suggest that older adults recognize the presentation of AI speech less frequently, rate AI speech as more natural, and are less able to discriminate between human and AI speech compared to younger adults. Research on speech perception in older adults may thus especially benefit from modern AI-based synthesized speech because, to them, AI speech feels much like spoken by a human.
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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 | 0.004 |
| 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) | 0.002 | 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".