Comparison of Bimodal and Bilateral Cochlear Implant Users on Speech Recognition With Competing Talker, Music Perception, Affective Prosody Discrimination, and Talker Identification
Bibliographic record
Abstract
In Brief Objectives: Despite excellent performance in speech recognition in quiet, most cochlear implant users have great difficulty with speech recognition in noise, music perception, identifying tone of voice, and discriminating different talkers. This may be partly due to the pitch coding in cochlear implant speech processing. Most current speech processing strategies use only the envelope information; the temporal fine structure is discarded. One way to improve electric pitch perception is to use residual acoustic hearing via a hearing aid on the nonimplanted ear (bimodal hearing). This study aimed to test the hypothesis that bimodal users would perform better than bilateral cochlear implant users on tasks requiring good pitch perception. Design: Four pitch-related tasks were used. Hearing in Noise Test (HINT) sentences spoken by a male talker with a competing female, male, or child talker. Montreal Battery of Evaluation of Amusia. This is a music test with six subtests examining pitch, rhythm and timing perception, and musical memory. Aprosodia Battery. This has five subtests evaluating aspects of affective prosody and recognition of sarcasm. Talker identification using vowels spoken by 10 different talkers (three men, three women, two boys, and two girls). Bilateral cochlear implant users were chosen as the comparison group. Thirteen bimodal and 13 bilateral adult cochlear implant users were recruited; all had good speech perception in quiet. Results: There were no significant differences between the mean scores of the bimodal and bilateral groups on any of the tests, although the bimodal group did perform better than the bilateral group on almost all tests. Performance on the different pitch-related tasks was not correlated, meaning that if a subject performed one task well they would not necessarily perform well on another. The correlation between the bimodal users' hearing threshold levels in the aided ear and their performance on these tasks was weak. Conclusions: Although the bimodal cochlear implant group performed better than the bilateral group on most parts of the four pitch-related tests, the differences were not statistically significant. The lack of correlation between test results shows that the tasks used are not simply providing a measure of pitch ability. Even if the bimodal users have better pitch perception, the real-world tasks used are reflecting more diverse skills than pitch. This research adds to the existing speech perception, language, and localization studies that show no significant difference between bimodal and bilateral cochlear implant users. Thirteen bimodal and 13 bilateral adult cochlear implant users were evaluated on tasks requiring good pitch perception: speech recognition with a competing talker, music perception, affective prosody discrimination, and talker identification. No significant differences were found between the mean scores of the bimodal and bilateral groups, although the bimodal group mostly scored better. Performance on the tasks was not correlated, suggesting that they were not providing a simple measure of pitch ability, but rather reflected more diverse real-world skills. This adds to existing speech perception, language, and localization studies showing no significant performance difference between bimodal and bilateral implant users.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".