Evaluation of Wideband Frequency Responses and Nonlinear Frequency Compression for Children with Cookie-Bite Audiometric Configurations
Bibliographic record
Abstract
BACKGROUND: Previous research has suggested that use of nonlinear frequency compression (NLFC) can improve audibility for high-frequency sounds and speech recognition of children with moderate to profound high-frequency hearing loss. Furthermore, previous studies have generally found no detriment associated with the use of NLFC. However, there have been no published studies examining the effect of NLFC on the performance of children with cookie-bite audiometric configurations. For this configuration of hearing loss, frequency-lowering processing will likely move high-frequency sounds to a lower frequency range at which a greater degree of hearing loss exists. PURPOSE: The purpose of this study was to evaluate and compare the effects of wideband amplification and NLFC on high-frequency audibility and speech recognition of children with cookie-bite audiometric configurations. RESEARCH DESIGN: This study consisted of a within-participant design with repeated measures across test conditions. STUDY SAMPLE: Seven children, ages 6-13 yr, with cookie-bite audiometric configurations and normal hearing or mild hearing loss at 6000 and 8000 Hz, were recruited. INTERVENTION: Participants were fitted with Phonak Nios S H2O III behind-the-ear hearing aids and Oticon Safari 300 behind-the-ear hearing aids. DATA COLLECTION: The participants were evaluated after three 4-to 6-wk intervals: (1) Phonak Nios S H2O III without NLFC, (2) Phonak Nios S H2O III with NLFC, and (3) Oticon Safari 300 with wideband frequency response extending to 8000 Hz. The order in which each technology was used was counterbalanced across participants. High-frequency audibility was evaluated by assessing aided thresholds (dB SPL) for warble tones and the high-frequency phonemes /sh/ and /s/. Speech recognition in quiet was measured with the University of Western Ontario (UWO) Plurals Test, the UWO Distinctive Features Difference (DFD) Test, and the Phoneme Perception Test vowel-consonant-vowel nonsense syllable test. Sentence recognition in noise was evaluated with the Bamford-Kowal-Bench Speech-In-Noise (BKB-SIN) Test. ANALYSIS: Repeated-measures analyses of variance were used to analyze the data collected in this study. The results across the three different conditions were compared. RESULTS: No difference in performance across conditions was observed for detection of high-frequency warble tones and the speech sounds /sh/ and /s/. No significant difference was seen across conditions for speech recognition in quiet when measured with the UWO Plurals Test, the UWO-DFD Test, and the Phoneme Perception Test vowel-consonant-vowel nonsense syllable test. Finally, there were also no differences across conditions on the BKB-SIN Test. CONCLUSIONS: These results suggest that NLFC does not degrade or improve audibility for and recognition of high-frequency speech sounds as well as sentence recognition in noise when compared with wideband amplification for children with cookie-bite audiometric configurations.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".