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Enregistrement W2034655421 · doi:10.3766/jaaa.20.2.6

Development of the North American Listening in Spatialized Noise‐Sentences Test (NA LiSN-S): Sentence Equivalence, Normative Data, and Test‐Retest Reliability Studies

2009· article· en· W2034655421 sur OpenAlexaboutno aff
Sharon Cameron, David K. Brown, Robert W. Keith, Jeffrey Martin, Charlene Watson, Harvey Dillon

Notice bibliographique

RevueJournal of the American Academy of Audiology · 2009
Typearticle
Langueen
DomaineNeuroscience
ThématiqueHearing Loss and Rehabilitation
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésEquivalence (formal languages)AudiologyActive listeningTest (biology)NormativeSentenceReliability (semiconductor)Noise (video)PsychologySpeech recognitionMedicineComputer scienceCommunicationNatural language processingMathematicsArtificial intelligencePolitical sciencePhysics

Résumé

récupéré en direct d'OpenAlex

BACKGROUND: The Listening in Spatialized Noise-Sentences test (LiSN-S) was originally developed in Australia to assess auditory stream segregation skills in children with suspected central auditory processing disorder (CAPD). The software produces a three-dimensional auditory environment under headphones. A simple repetition-response protocol is utilized to determine speech reception thresholds (SRTs) for sentences presented from 0 degrees azimuth in competing speech. The competing speech (looped children's stories) is manipulated with respect to its location (0 degrees vs. +90 degrees and -90 degrees azimuth) and the vocal quality of the speaker(s) (same as, or different to, the speaker of the target stimulus). Performance is measured as two SRT and three advantage measures. The advantage measures represent the benefit in dB gained when either talker, spatial, or both talker and spatial cues combined are incorporated in the maskers. PURPOSE: The objective of this research was to develop a version of the LiSN-S suitable for use in the United States and Canada. The original sentences and children's stories were reviewed for unfamiliar semantic items and rerecorded by native North American speakers. RESEARCH DESIGN: In a descriptive design, a sentence equivalence study was conducted to determine the relative intelligibility of the rerecorded sentences and adjust the amplitude of the sentences for equal intelligibility. Normative data and test-retest reliability data were then collected. STUDY SAMPLE: Twenty-four children with normal hearing aged 8 years, 3 months, to 10 years, 0 months, took part in the sentence equivalence study. Seventy-two normal-hearing children aged 6 years, 2 months, to 11 years, 10 months, took part in the normative data study. Thirty-six children returned between two and three months after the initial assessment for retesting. Participants were recruited from sites in Cincinnati, Dallas, and Calgary. RESULTS: The sentence equivalence study showed that post-adjustment, sentence intelligibility increased by 18.7 percent for each 1 dB increase in signal-to-noise ratio. Analysis of the normative data revealed no significant differences on any performance measure as a consequence of data collection site or gender. Inter- and intra-participant variation was minimal. A trend of improved performance as a function of increasing age was found across performance measures, and cutoff scores, calculated as two standard deviations below the mean, were adjusted for age. Test-retest differences were not significant on any measure of the North American (NA) LiSN-S (p ranging from .080 to .862). Mean test-retest differences on the various NA LiSN-S performance measures ranged from 0.1 dB to 0.6 dB. One-sided critical difference scores calculated from the retest data ranged from 3 to 3.9 dB. These scores, which take into account mean practice effects and day-to-day fluctuations in performance, can be used to determine whether a child has improved on the NA LiSN-S on retest. CONCLUSIONS: The NA LiSN-S is a potentially valuable tool for assessing auditory stream segregation skills in children. The availability of one-sided critical difference scores makes the NA LiSN-S useful for monitoring listening performance over time and determining the effects of maturation, compensation (such as an assistive listening device), or remediation.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,015
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche, Études des sciences et des technologies
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,163
Score d'incertitude au seuil0,999

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,015
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,004
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,055
Tête enseignante GPT0,354
Écart entre enseignants0,299 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations51
Publié2009
Routes d'admission1
Résumé présentoui

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