Saliency of Vowel Features in Neural Responses of Cochlear Implant Users
Bibliographic record
Abstract
Cochlear implants restore hearing in deaf individuals, but speech perception remains challenging. Poor discrimination of spectral components is thought to account for limitations of speech recognition in cochlear implant users. We investigated how combined variations of spectral components along two orthogonal dimensions can maximize neural discrimination between two vowels, as measured by mismatch negativity. Adult cochlear implant users and matched normal-hearing listeners underwent electroencephalographic event-related potentials recordings in an optimum-1 oddball paradigm. A standard /a/ vowel was delivered in an acoustic free field along with stimuli having a deviant fundamental frequency (+3 and +6 semitones), a deviant first formant making it a /i/ vowel or combined deviant fundamental frequency and first formant (+3 and +6 semitones /i/ vowels). Speech recognition was assessed with a word repetition task. An analysis of variance between both amplitude and latency of mismatch negativity elicited by each deviant vowel was performed. The strength of correlations between these parameters of mismatch negativity and speech recognition as well as participants' age was assessed. Amplitude of mismatch negativity was weaker in cochlear implant users but was maximized by variations of vowels' first formant. Latency of mismatch negativity was later in cochlear implant users and was particularly extended by variations of the fundamental frequency. Speech recognition correlated with parameters of mismatch negativity elicited by the specific variation of the first formant. This nonlinear effect of acoustic parameters on neural discrimination of vowels has implications for implant processor programming and aural rehabilitation.
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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) | 0.002 | 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".