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Record W2606715714 · doi:10.3766/jaaa.16001

Prosody Perception and Production in Children with Hearing Loss and Age- and Gender-Matched Controls

2016· article· en· W2606715714 on OpenAlexaboutno aff
Rose Thomas Kalathottukaren, Suzanne C. Purdy, Elaine Ballard

Bibliographic record

VenueJournal of the American Academy of Audiology · 2016
Typearticle
Languageen
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsnot available
Fundersnot available
KeywordsAudiologyProsodyHearing lossPsychologyPerceptionSpeech perceptionAudiometrySpeech productionMedicineSpeech recognitionComputer science

Abstract

fetched live from OpenAlex

BACKGROUND: Auditory development in children with hearing loss, including the perception of prosody, depends on having adequate input from cochlear implants and/or hearing aids. Lack of adequate auditory stimulation can lead to delayed speech and language development. Nevertheless, prosody perception and production in people with hearing loss have received less attention than other aspects of language. The perception of auditory information conveyed through prosody using variations in the pitch, amplitude, and duration of speech is not usually evaluated clinically. PURPOSE: This study (1) compared prosody perception and production abilities in children with hearing loss and children with normal hearing; and (2) investigated the effect of age, hearing level, and musicality on prosody perception. RESEARCH DESIGN: Participants were 16 children with hearing loss and 16 typically developing controls matched for age and gender. Fifteen of the children with hearing loss were tested while using amplification (n = 9 hearing aids, n = 6 cochlear implants). Six receptive subtests of the Profiling Elements of Prosody in Speech-Communication (PEPS-C), the Child Paralanguage subtest of Diagnostic Analysis of Nonverbal Accuracy 2 (DANVA 2), and Contour and Interval subtests of the Montreal Battery of Evaluation of Amusia (MBEA) were used. Audio recordings of the children's reading samples were rated using a perceptual prosody rating scale by nine experienced listeners who were blinded to the children's hearing status. STUDY SAMPLE: Thirty two children, 16 with hearing loss (mean age = 8.71 yr) and 16 age- and gender-matched typically developing children with normal hearing (mean age = 8.87 yr). DATA COLLECTION AND ANALYSIS: Assessments were completed in one session lasting 1-2 hours in a quiet room. Test items were presented using a laptop computer through loudspeaker at a comfortable listening level. For children with hearing loss using hearing instruments, all tests were completed with hearing devices set at their everyday listening setting. RESULTS: All PEPS-C subtests and total scores were significantly lower for children with hearing loss compared to controls (p < 0.05). The hearing loss group performed more poorly than the control group in recognizing happy, sad, and fearful emotions in the DANVA 2 subtest. Musicality (composite MBEA scores and musical experience) was significantly correlated with prosody perception scores, but this link was not evident in the regression analyses. Regression modeling showed that age and hearing level (better ear pure-tone average) accounted for 55.4% and 56.7% of the variance in PEPS-C and DANVA 2 total scores, respectively. There was greater variability for the ratings of pitch, pitch variation, and overall impression of prosody in the hearing loss group compared to control group. Prosody perception (PEPS-C and DANVA 2 total scores) and ratings of prosody production were not correlated. CONCLUSIONS: Children with hearing loss aged 7-12 yr had significant difficulties in understanding different aspects of prosody and were rated as having more atypical prosody overall than controls. These findings suggest that clinical assessment and speech-language therapy services for children with hearing loss should be expanded to target prosodic difficulties. Future studies should investigate whether musical training is beneficial for improving receptive prosody skills.

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 score0.591
Threshold uncertainty score0.384

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 studies0.0000.001
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 head0.029
GPT teacher head0.300
Teacher spread0.272 · 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

Citations35
Published2016
Admission routes1
Has abstractyes

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