Recognizing voices through a cochlear implant: A systematic review
Bibliographic record
Abstract
Objective: Some cochlear implant (CI) users report having difficulty accessing indexical information in the speech signal, presumably due to the transformation from acoustic to electric signal in CI devices. The purpose of this paper was to systematically review and evaluate the existing research on talker perception in CI users. Specifically, we reviewed the performance of CI users in talker discrimination, gender identification, and talker recognition tasks in relation to performance by normal-hearing (NH) listeners. We also examined the different factors (such asparticipant, hearing and device characteristics) that might influence talker perception.Design: We completed a systematic search of the literature with select keywords using citation aggregation software to search Google Scholar. We included primary reports that had at least one group of participants with cochlear implants, and had an experimental task that measured talker or voice perception. Each included study was also evaluated for quality of evidence.Results: The initial search resulted in 1239 references, which were first screened for inclusion and then evaluated in full. Thirty-nine studies examining talker identification, talker discrimimnation, and gender discrimination were included in the final review. The majority of studies were focused on adult postlingual cochlear implant users, with a few studies focused on prelingual implant users. As a group, CI users generally performed above chance in talker perception tasks, but performed worse than NH controls. Nonetheless, a subset of CI users reached the same level of performance as NH participants. CI users relied more heavily on fundamental frequency over vocal tract length cues to distinguish talkers compared to NH listeners. Within groups of CI users, there is moderate evidence for a bimodal benefit for talker perception, and there are mixed findings about the effects of hearing experience. Performance in talker discrimination tasks was related to other linguistic tasks, including word recognition. Conclusion: The current review highlights the challenges faced by CI users in tracking and recognizing voices and how they adapt to it. There is clear evidence that CI users can process indexical information, albeit differently and more effortfully than NH listeners. Recent work has begun to describe some of the factors that might ease the challenges of talker perception in CI users, but further high-quality research is needed to disentangle some of the mixed findings. We conclude by suggesting some future avenues of research to optimize real-world speech outcomes.
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".