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Record W4312729122 · doi:10.1121/2.0001664

The Speech and Language Resource Bank: A central index of resources for speech science research and education

2020· article· en· W4312729122 on OpenAlexaff
Charles Redmon, Matthew C. Kelley, Benjamin V. Tucker

Bibliographic record

VenueProceedings of meetings on acoustics · 2020
Typearticle
Languageen
FieldComputer Science
TopicSpeech Recognition and Synthesis
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsComputer scienceDocumentationResource (disambiguation)Field (mathematics)Fragmentation (computing)Data scienceWorld Wide Web

Abstract

fetched live from OpenAlex

The increasing proliferation of code, data, and programming language libraries for use in speech science research raises concerns over the maintenance and organization of these disparate resources. These concerns have motivated us to develop the Speech and Language Resource Bank (SLRB; www.slrb.net): a federated repository of resources for speech analysis, data, education, and experimentation. Our primary goal for this repository is to serve as a central point from which researchers and teachers in the speech sciences can find and access resources from the community. In the future we aim to use the SLRB to establish standards for resource development and documentation, as well as providing opportunities for the development of larger meta-packages that can mitigate the increasing duplication and fragmentation of work in the field.

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)0.005
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.582
Threshold uncertainty score0.580

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.005
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 science0.0010.000
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 head0.031
GPT teacher head0.302
Teacher spread0.271 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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