The contribution of visual information to the perception of speech in noise with and without informative temporal fine structure
Bibliographic record
Abstract
Understanding what is said in demanding listening situations is assisted greatly by looking at the face of a talker. Previous studies have observed that normal-hearing listeners can benefit from this visual information when a talker's voice is presented in background noise. These benefits have also been observed in quiet listening conditions in cochlear-implant users, whose device does not convey the informative temporal fine structure cues in speech, and when normal-hearing individuals listen to speech processed to remove these informative temporal fine structure cues. The current study (1) characterised the benefits of visual information when listening in background noise; and (2) used sine-wave vocoding to compare the size of the visual benefit when speech is presented with or without informative temporal fine structure. The accuracy with which normal-hearing individuals reported words in spoken sentences was assessed across three experiments. The availability of visual information and informative temporal fine structure cues was varied within and across the experiments. The results showed that visual benefit was observed using open- and closed-set tests of speech perception. The size of the benefit increased when informative temporal fine structure cues were removed. This finding suggests that visual information may play an important role in the ability of cochlear-implant users to understand speech in many everyday situations. Models of audio-visual integration were able to account for the additional benefit of visual information when speech was degraded and suggested that auditory and visual information was being integrated in a similar way in all conditions. The modelling results were consistent with the notion that audio-visual benefit is derived from the optimal combination of auditory and visual sensory cues.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".