Effects of audiovisual asynchrony on speech intelligibility in typically hearing adults and cochlear implant users
Bibliographic record
Abstract
PURPOSE: Speech articulators produce auditory cues conveyed by the voice as well as visual cues that are perceptually integrated to form a highly salient, unified audiovisual signal. How individuals make use of these cues depends on listener- and stimulus-specific factors. While both typically-hearing listeners and cochlear implant (CI) users typically integrate synchronous auditory and visual cues, CI users are more likely to use visual cues to enhance perception. This may be especially true when stimulus characteristics such as temporal alignment of the auditory and visual cues are degraded, affecting intelligibility. Here, we examine the interaction between these listener and stimulus characteristics by quantifying how asynchrony affects speech intelligibility in CI users and typically-hearing controls.METHODS: 15 CI users (aged 59.9 ± 15.5) and 31 typically-hearing control participants (aged 61.2 ± 15.9) were presented with audio-only, visual-only, synchronous audiovisual, and asynchronous (100, 200, 300, and 400 ms of temporal offset in both audio- and visual-leads) audiovisual sentences in multi-talker babble (+9 dB SNR for CI users, 0 dB SNR for controls). Intelligibility was quantified as the proportion of keywords correctly reported. Benefits associated with multisensory integration across asynchronies were computed by comparing performance to unisensory conditions.RESULTS: Intelligibility and multisensory gain were strongest near synchrony for both groups, tapering off as audiovisual asynchrony increased. CI users showed larger decrements in performance with increasing asynchrony than controls. At the largest audio-leading asynchrony, CI users’ performance continued to decrease while controls improved relative to smaller asynchronies.CONCLUSION: Typically-hearing listeners benefit from multisensory integration over a broader range of speech asynchronies than CI users. Interestingly, at extreme asynchronies CI users appear to be impaired by visual speech cues compared to typically hearing individuals, suggesting they may attempt to use visual speech cues to inform intelligibility even when they occur outside of the range of asynchronies over which integration typically occurs. These findings align with literature on increased visual bias in CI users, and raise questions regarding potential compensatory mechanisms for speech intelligibility.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".