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Record W1645872473

Phonetic and phonological influence of a speech-Impaired speaker on Rhythm

2011· article· en· W1645872473 on OpenAlexaffvenueabout
Gurnikaita Chhina, Tae-Jin Yoon, Karin R. Humphreys

Bibliographic record

VenueCanadian acoustics · 2011
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsMcMaster University
Fundersnot available
KeywordsSyllableStress (linguistics)LinguisticsSpeech recognitionPsychologyRhythmConnected speechSpeech errorWord (group theory)Computer scienceSpeech productionAudiologyAcousticsMedicine
DOInot available

Abstract

fetched live from OpenAlex

A study showing a detailed phonetic analyses of a 61 year-old monolingual female English speaker is presented. There is considerable variability among reported cases of FAS in terms of phonetic characteristics and impairments. The speaker, LA, is a monolingual English-speaking Canadian Woman and she was 61-year old when the data were collected. One day three years after the accident, her family member observed noticeable changes in her speech such as word searching, stuttering, and a robotic style of speech. Upon request by the family members, the third author visited the speaker and collected the speech recordings through sessions with the patient The collected recordings range from simple read sentences to passages, and to spontaneous description of pictures. Typical stress-timed languages such as English have complex syllable structure and tend to have reduced vowels in unstressed positions.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.041
GPT teacher head0.237
Teacher spread0.196 · 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 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
Published2011
Admission routes3
Has abstractyes

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