Children's Perception of Speaker Identity from Spectrally Degraded Input
Bibliographic record
Abstract
Speaker identification is a challenge for cochlear implant users because their prosthesis restricts access to the cues that underlie natural voice quality. The present thesis examined speaker recognition in the context of spectrally degraded sentences. The listeners of interest were child implant users who were prelingually deaf as well as hearing children and adults who listened to speech via vocoder simulations of implant processing. Study 1 focused on child implant users' identification of a highly salient speaker—the mother (identified as mother)—and unfamiliar speakers varying in age and gender (identified as man, woman, or girl). In a further experiment, children were required to differentiate their mother's voice from the voices of unfamiliar women. Young hearing children were tested on the same tasks and stimuli. Although child implant users performed more poorly than hearing children overall, they successfully differentiated their mother's voice from other voices. In fact, their performance surpassed expectations based on previous studies of child and adult implant users. Even when natural variations in speaking style were reduced, child implant users successfully identified the speakers. The findings imply that person-specific differences in articulatory style contributed to implanted children's successful performance. Study 2 used vocoder simulations of cochlear implant processing to vary the spectral content of sentences produced by the man, woman, and girl from Study 1. The ability of children (5-7 years and 10-12 years) and adults with normal hearing to identify the speakers was affected by the level of spectral degradation and by the gender of the speaker. Female voices were more difficult to identify than was the man's voice, especially for the younger children. In some respects, hearing individuals' identification of degraded voices was poorer than that of child implant users in Study 1. In a further experiment, hearing children and adults were required to provide verbatim repetitions of spectrally degraded sentences. Their performance on this task greatly exceeded their performance on speaker identification at comparable levels of spectral degradation. The present findings underline the importance of ecologically valid materials and methods when assessing speaker identification, especially in children. Moreover, they raise questions about the efficacy of vocoder models for the study of speaker identification in cochlear 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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".