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Record W1975513957 · doi:10.1097/aud.0b013e31819ec93a

The Effect of Instantaneous Input Dynamic Range Setting on the Speech Perception of Children with the Nucleus 24 Implant

2009· article· en· W1975513957 on OpenAlexaff
Lisa S. Davidson, Margaret W. Skinner, Beth A. Holstad, Beverly T. Fears, Marie K. Richter, Margaret Matusofsky, Christine Brenner, Timothy A. Holden, Amy Lynn Birath, Jerrica Kettel, Susan Scollie

Bibliographic record

VenueEar and Hearing · 2009
Typearticle
Languageen
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsWestern University
FundersNational Institute on Deafness and Other Communication DisordersCity University of New York
KeywordsLoudnessAudiologySpeech perceptionCochlear implantConsonantHearing aidSpeech recognitionNoise (video)Intelligibility (philosophy)PsychologyMathematicsPerceptionMedicineComputer scienceArtificial intelligenceVowel

Abstract

fetched live from OpenAlex

In Brief Objective: The purpose of this study was to examine the effects of a wider instantaneous input dynamic range (IIDR) setting on speech perception and comfort in quiet and noise for children wearing the Nucleus 24™ implant system and the Freedom™ speech processor. In addition, children's ability to understand soft and conversational level speech in relation to aided sound-field thresholds was examined. Design: Thirty children (age, 7 to 17 years) with the Nucleus 24 cochlear implant system and the Freedom speech processor with two different IIDR settings (30 versus 40 dB) were tested on the Consonant Nucleus Consonant (CNC) word test at 50 and 60 dB SPL, the Bamford-Kowal-Bench Speech in Noise Test, and a loudness rating task for four-talker speech noise. Aided thresholds for frequency-modulated tones, narrowband noise, and recorded Ling sounds were obtained with the two IIDRs and examined in relation to CNC scores at 50 dB SPL. Speech Intelligibility Indices were calculated using the long-term average speech spectrum of the CNC words at 50 dB SPL measured at each test site and aided thresholds. Results: Group mean CNC scores at 50 dB SPL with the 40 IIDR were significantly higher (p < 0.001) than with the 30 IIDR. Group mean CNC scores at 60 dB SPL, loudness ratings, and the signal to noise ratios-50 for Bamford-Kowal-Bench Speech in Noise Test were not significantly different for the two IIDRs. Significantly improved aided thresholds at 250 to 6000 Hz as well as higher Speech Intelligibility Indices afforded improved audibility for speech presented at soft levels (50 dB SPL). Conclusion: These results indicate that an increased IIDR provides improved word recognition for soft levels of speech without compromising comfort of higher levels of speech sounds or sentence recognition in noise. The ability of children to understand speech at soft and conversational levels with two instantaneous input dynamic range (IIDR) settings (30 & 40) on the Freedom Cochlear Implant Processor™ was examined for thirty children (age 7-17). Aided threshold and recorded speech perception testing was conducted using FM tones, noise-bands, Ling 6 sounds, loudness scaling, CNC words at 50 & 60 dB SPL and the BKB-SIN. Results revealed that mean group thresholds were 6-8 dB better with the 40 IIDR. Group mean CNC word scores at 50 dB SPL were significantly better with the 40 IIDR (59.2% vs. 47.8%) while the CNC word scores at 60 dB SPL and BKB-SIN scores at 65 dB SPL were not significantly different. It was concluded that an IIDR of 40 provided significantly better sound-field thresholds and this enabled the children to achieve significantly better CNC word scores at 50 dB SPL by making more sounds audible and recognizable. The BKB-SIN SNR-50 and loudness ratings results were not significantly different between the two IIDR settings, thus improved aided thresholds and recognition of soft speech does not compromise recognition of speech in noise or comfort of higher level sounds.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.964
Threshold uncertainty score0.276

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.009
GPT teacher head0.245
Teacher spread0.236 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations40
Published2009
Admission routes1
Has abstractyes

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