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Investigations of speech segmentation: addressing the writing bias in language research

2008· article· en· W2398647454 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueExLing Conferences · 2008
Typearticle
Languageen
FieldPsychology
TopicPhonetics and Phonology Research
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsRhythmComputer scienceSpeech segmentationSpeech recognitionParsingSegmentationLinguisticsNatural language processingVowelArtificial intelligence

Abstract

fetched live from OpenAlex

We address the problem of defining universal processes of speech segmentation in view of criticisms that conceptual linguistic units derive from western writing. A synthesis of our recent experimental studies is presented bearing on processes of serial-order and rhythmic grouping. First, on how serial-order operates, we use EMG and speech-motion data to show that “consonant-vowel” orders reflect contraction-relaxation cycles (not separate segments). Second, on how sequences of sounds come to form units in language learning, we discuss behavioural data suggesting a link between rhythm groups in speech and grouping effects on memory of speech sounds. Preliminary EEG data is then presented to substantiate the view of an on-line parsing of rhythm groups with effects on memory traces of lexemes.

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.

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)0.002
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 score0.242
Threshold uncertainty score0.704

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.631
GPT teacher head0.526
Teacher spread0.104 · 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