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Record W4240038455 · doi:10.1121/1.4799754

Comprehending speech at artificially enhanced rates

2013· article· en· W4240038455 on OpenAlexafffund
Lucia Da Silva, Adriano Vilela Barbosa, Eric Vatikiotis‐Bateson

Bibliographic record

VenueProceedings of meetings on acoustics · 2013
Typearticle
Languageen
FieldComputer Science
TopicSpeech and Audio Processing
Canadian institutionsUniversity of British Columbia
FundersSocial Sciences and Humanities Research Council of CanadaNatural Sciences and Engineering Research Council of Canada
KeywordsActive listeningUtteranceSpeech recognitionComputer sciencePerceptionDuration (music)Focus (optics)Speech perceptionNatural (archaeology)Baseline (sea)Motor theory of speech perceptionAudiologyPsychologyCommunicationAcousticsHistory

Abstract

fetched live from OpenAlex

This study is part of a larger study comparing the production, perception, and long-term comprehension of natural fast speech and artificially sped-up speech.In the 1950?s, Fairbanks and colleagues made the interesting claim that artificially compressing speech to half its duration (twice the rate) and listening to it twice is comprehended better than listening to the original natural speed utterance once.Since this finding has been disputed [T.Sticht, J. Expr.Ed., 37, 60-62, 1969], we focus here on providing baseline results for perception of naturally produced speech at conversational and fast rate and of speech sped up to twice its original speed.Specifically, we compare how well the different stimuli are perceived when presented once, twice in rapid succession, and twice with delays of hours and days between presentations.We evaluate perception for both audio-only and audiovisual stimuli.

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)0.007
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.013
GPT teacher head0.240
Teacher spread0.228 · 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

Citations0
Published2013
Admission routes2
Has abstractyes

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