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Record W2277822424

Prosody in typical and clinical populations: Children and adults with hearing loss

2015· dissertation· en· W2277822424 on OpenAlexaboutno aff
Rose Thomas Kalathottukaren

Bibliographic record

VenueResearchSpace (University of Auckland) · 2015
Typedissertation
Languageen
FieldMedicine
TopicVoice and Speech Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsProsodyAudiologyHearing lossPsychologyMedicineComputer scienceSpeech recognition
DOInot available

Abstract

fetched live from OpenAlex

Aims: Aims of this doctoral thesis were to: 1) evaluate published tools for assessing prosodic skills in children and adults, 2) investigate age effects on different aspects of prosody perception in typically developing children and report normative performance for New Zealand-English speaking 7-12 year olds, 3) compare prosody perception and production in children with hearing loss and age- and gender-matched children with normal hearing, 4) examine the effects of age, hearing level, and musicality on children’s prosody perception, and 5) investigate prosody perception and musical pitch discrimination in adults using cochlear implants. Methods: Published tools were identified through searching online databases, bibliographies of relevant articles and contacting authors. Six receptive subtests of Profiling Elements of Prosody in Speech-Communication (PEPS-C) and Child Paralanguage and Adult Paralanguage subtests of Diagnostic Analysis of Non Verbal Accuracy 2 (DANVA 2) were used as prosody perception measures. Musical pitch discrimination was assessed using Contour and Interval subtests of the Montreal Battery of Evaluation of Amusia (MBEA). Prosody productions were rated using the perceptual prosody rating scales. Results: The literature review identified nine prosody assessment tools available for use with children and adults that were appraised for their intended purpose, target population, domains of prosody assessed, feasibility, and psychometric properties. The review highlighted the need to continue to develop and test tools for effective and comprehensive assessment of prosodic skills. The second study revealed that prosodic competence develops significantly between the ages of 7 and 11 years. Results from the PEPS-C test revealed a differential pattern of acquisition for different aspects of prosody, with 7-8 year olds being significantly poorer than 11-12 year olds on Chunking and Contrastive Stress subtests. Performance on the DANVA 2 test of affective prosody perception differed significantly across emotional categories (angry > happy > sad > fearful) and the level of emotion intensity (better scores for high emotion intensity items). The third study showed that children with hearing loss aged 7 to 12 years performed significantly poorer than controls on PEPS-C and DANVA 2 tests. Prosody perception scores were significantly correlated with age, hearing level, and musicality. Prosody production evaluated using perceptual rating scales showed greater variation in perceptual ratings of pitch, pitch variation, and overall prosody in the hearing loss group compared to the control group. Adults using cochlear implants performed significantly poorer than adult normative values reported for PEPS-C and DANVA 2 tests and the majority performed at chance on MBEA tasks. Conclusions: The relatively small number of tools available to evaluate prosody compared to other aspects of language suggests that prosody is often overlooked in terms of formal language assessment. The normative results reported for New Zealand-English speaking children will be useful when assessing prosodic difficulties in children with hearing loss or autism spectrum disorder. Together, the studies on children and adults with hearing loss suggest that clinical assessment and therapy services for people with hearing loss should be expanded to target prosodic difficulties.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.986

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 integrity0.0000.001
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 head0.040
GPT teacher head0.355
Teacher spread0.315 · 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

Citations0
Published2015
Admission routes1
Has abstractyes

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