Neural Tracking Measures of Speech Intelligibility: Manipulating Intelligibility while Keeping Acoustics Unchanged
Bibliographic record
Abstract
Abstract Neural speech tracking has advanced our understanding of how our brains rapidly map an acoustic speech signal onto linguistic representations and ultimately meaning. It remains unclear, however, how speech intelligibility is related to the corresponding neural responses. Many studies addressing this question vary the level of intelligibility by manipulating the acoustic waveform, but this makes it difficult to cleanly disentangle effects of intelligibility from underlying acoustical confounds. Here, using magnetoencephalography (MEG) recordings, we study neural measures of speech intelligibility by manipulating intelligibility while keeping the acoustics strictly unchanged. Acoustically identical degraded speech stimuli (three-band noise vocoded, ∼20 s duration) are presented twice, but the second presentation is preceded by the original (non-degraded) version of the speech. This intermediate priming, which generates a ‘pop-out’ percept, substantially improves the intelligibility of the second degraded speech passage. We investigate how intelligibility and acoustical structure affects acoustic and linguistic neural representations using multivariate Temporal Response Functions (mTRFs). As expected, behavioral results confirm that perceived speech clarity is improved by priming. TRF analysis reveals that auditory (speech envelope and envelope onset) neural representations are not affected by priming, but only by the acoustics of the stimuli (bottom-up driven). Critically, our findings suggest that segmentation of sounds into words emerges with better speech intelligibility, and most strongly at the later (∼400 ms latency) word processing stage, in prefrontal cortex (PFC), in line with engagement of top-down mechanisms associated with priming. Taken together, our results show that word representations may provide some objective measures of speech comprehension. Significance Statement Electrophysiological studies have shown that brain tracks different speech features. How these neural tracking measures are modulated by speech intelligibility, however, remained elusive. Using noise-vocoded speech and a priming paradigm, we disentangled the neural effects of intelligibility from the underlying acoustical confounds. Neural intelligibility effects are analyzed at both acoustic and linguistic level using multivariate Temporal Response Functions. Here, we find evidence for an effect of intelligibility and engagement of top-down mechanisms, but only in responses to lexical structure of the stimuli, suggesting that lexical responses are strong candidates for objective measures of intelligibility. Auditory responses are not influenced by intelligibility but only by the underlying acoustic structure of the stimuli.
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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 | 0.000 | 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) | 0.001 | 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".