Children’s Recognition of Spectrally Degraded Cartoon Voices
Bibliographic record
Abstract
In Brief Objectives: Although the spectrally degraded input provided by cochlear implants (CIs) is sufficient for speech perception in quiet, it poses problems for talker identification. The present study examined the ability of normally hearing (NH) children and child CI users to recognize cartoon voices while listening to spectrally degraded speech. Design: In Experiment 1, 5- to 6-year-old NH children were required to identify familiar cartoon characters in a three-alternative, forced-choice task without feedback. Children heard sentence-length utterances at six levels of spectral degradation (noise-vocoded utterances with 4, 8, 12, 16, and 24 frequency bands and the original or unprocessed stimuli). In Experiment 2, child CI users 4 to 7 years of age and a control sample of 4- to 5-year-old NH children were required to identify the unprocessed stimuli from Experiment 1. Results: NH children in Experiment 1 identified the voices significantly above chance levels, and they performed more accurately with increasing spectral information. Practice with stimuli that had greater spectral information facilitated performance on subsequent stimuli with lesser spectral information. In Experiment 2, child CI users successfully recognized the cartoon voices with slightly lower accuracy (0.90 proportion correct) than NH peers who listened to unprocessed utterances (0.97 proportion correct). Conclusions: The findings indicate that both NH children and child CI users can identify cartoon voices under conditions of severe spectral degradation. In such circumstances, children may rely on talker-specific phonetic detail to distinguish one talker from another. This paper examines the ability of children with normal hearing (NH) and children with cochlear implants (CIs) to identify familiar cartoon voices in a forced-choice task. In Experiment 1, NH children identified familiar cartoon characters from unprocessed utterances as well as from vocoded utterances with 4-24 frequency bands. Performance was above chance levels, but increasing spectral detail enhanced accuracy. In Experiment 2, young deaf children with bilateral CIs identified the same cartoon characters from unprocessed utterances. These findings indicate that NH children and child CI users have representations of cartoon voices that support talker recognition under conditions of spectral degradation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".