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Record W1996861904 · doi:10.1121/1.2942936

Effects of frequency shifts on the identification of vowels and words in sentences

2007· article· en· W1996861904 on OpenAlexaff
Peter F. Assmann, Terrance M. Nearey

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2007
Typearticle
Languageen
FieldComputer Science
TopicSpeech Recognition and Synthesis
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsVowelEnvelope (radar)Context (archaeology)MathematicsSentenceAcousticsSet (abstract data type)Identification (biology)Speech recognitionPhysicsComputer scienceTelecommunicationsNatural language processingGeology

Abstract

fetched live from OpenAlex

Studies of the effects of frequency shifts on vowel identification have shown a drop in accuracy when the spectrum envelope is shifted up or down, and when the fundamental frequency (F0) is raised or lowered. We have found an interaction between F0 and spectrum envelope shifts: Performance is better for vowels with matched shifts (both F0 and spectrum envelope shifted in the same direction) compared to mismatched shifts (F0 shifted up and spectrum envelope shifted down or vice versa). The aim of the present study was to determine the extent to which these effects persist in sentence context. The STRAIGHT vocoder was used to process a set of sentences from the HINT test using the same scale factors as in the vowel identification experiment. Word recognition scores generally followed the same pattern as vowel identification, with poorer performance for lowered F0 and raised spectrum envelope, and the lowest scores in conditions with high F0 and downward shifts in spectrum envelope, compared to the corresponding matched shifts. [Work supported by NSF and SSHRC.]

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 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)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.457
Threshold uncertainty score0.189

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.012
GPT teacher head0.248
Teacher spread0.236 · 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 teacher head, 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

Citations2
Published2007
Admission routes1
Has abstractyes

Explore more

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