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Record W4296887694 · doi:10.18280/isi.270415

Designing a Two - Dimensional Animation for Verbal Apraxia Therapy for Children with Verbal Apraxia of Speech

2022· article· en· W4296887694 on OpenAlexvenueno aff
Muhammad Taufik Hidayat, Sarni Suhaila Rahim, Shahril Parumo, Nurul Najihah A’bas, Muhammad ‘Ammar Muhammad Sani, Hilmi Abdul Aziz

Bibliographic record

VenueIngénierie des systèmes d information · 2022
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsnot available
FundersFaculty of Information and Communication Technology, Mahidol UniversityUniversiti Teknikal Malaysia Melaka
KeywordsAnimationApraxiaCognitive psychologyComputer scienceNonverbal communicationComputer facial animationPsychologyUsabilityCognitionComputer animationMultimediaHuman–computer interactionAphasiaDevelopmental psychology

Abstract

fetched live from OpenAlex

Verbal Apraxia, also called Apraxia of Speech (AOS) is a speech sound condition that affects a person’s ability to translate conscious speech goals into motor plans, resulting in limited and difficult communication. This article presents an investigation on the designing of a 2D animation and its use as a therapy for Verbal Apraxia. This paper aims to investigate animation principles and to design and develop an animation video as a therapeutic solution. The expected outcome of this paper is a comprehensive analysis of the cognitive training in verbal therapy while focusing on spreading awareness of verbal apraxia towards society. In conclusion, this animation video runs successfully and meets all the objectives completely. Therefore, this proposed 2D animation is expected to contribute as a teaching syllabus for special needs schools and produce great usability for the users.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.023
GPT teacher head0.264
Teacher spread0.241 · 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 source (direct Gemma or distilled Codex), 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

Citations7
Published2022
Admission routes1
Has abstractyes

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