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Record W1966919323 · doi:10.1121/1.3672697

Effect of cochlear implants on children’s perception and production of speech prosody

2012· article· en· W1966919323 on OpenAlexaff
Takayuki Nakata, Sandra E. Trehub, Yukihiko Kanda

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2012
Typearticle
Languageen
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsProsodyImitationPsychologyAudiologyPerceptionDisappointmentMandarin ChineseSpeech productionSurpriseSpeech perceptionSpeech recognitionLinguisticsCommunicationMedicineComputer scienceSocial psychology

Abstract

fetched live from OpenAlex

Japanese 5- to 13-yr-olds who used cochlear implants (CIs) and a comparison group of normally hearing (NH) Japanese children were tested on their perception and production of speech prosody. For the perception task, they were required to judge whether semantically neutral utterances that were normalized for amplitude were spoken in a happy, sad, or angry manner. The performance of NH children was error-free. By contrast, child CI users performed well below ceiling but above chance levels on happy- and sad-sounding utterances but not on angry-sounding utterances. For the production task, children were required to imitate stereotyped Japanese utterances expressing disappointment and surprise as well as culturally typically representations of crow and cat sounds. NH 5- and 6-year-olds produced significantly poorer imitations than older hearing children, but age was unrelated to the imitation quality of child CI users. Overall, child CI user's imitations were significantly poorer than those of NH children, but they did not differ significantly from the imitations of the youngest NH group. Moreover, there was a robust correlation between the performance of child CI users on the perception and production tasks; this implies that difficulties with prosodic perception underlie their difficulties with prosodic imitation.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.432
Threshold uncertainty score0.231

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.012
GPT teacher head0.280
Teacher spread0.268 · 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 designBench or experimental
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

Citations97
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of the Acoustical Society of AmericaSame topicHearing Loss and RehabilitationFrench-language works237,207