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Record W3154903128 · doi:10.1002/9781119184096.ch4

Perceptual Control of Speech

2021· other· en· W3154903128 on OpenAlexaff
Kevin G. Munhall, Anja‐Xiaoxing Cui, Ellen M O'Donoghue, Steven J. Lamontagne, David Lutes

Bibliographic record

Venuenot available
Typeother
Languageen
FieldPsychology
TopicAction Observation and Synchronization
Canadian institutionsUniversity of British ColumbiaQueen's University
Fundersnot available
KeywordsPerceptionAuditory feedbackSensory systemEfference copySpeech productionControl (management)Speech recognitionMotor theory of speech perceptionComputer scienceNeurocomputational speech processingCorollarySpeech perceptionPerceptual learningPsychologyCommunicationCognitive psychologyArtificial intelligenceNeuroscience

Abstract

fetched live from OpenAlex

Like singing, speech is governed by a control system that requires sensory information about the effects of its actions, and the major source of this sensory feedback is the auditory system. This chapter addresses a number of issues related to the perceptual control of speech production. The study of postlingually deafened individuals represents the best window onto the role played by auditory feedback in a well-developed human control system. The chapter reviews what is known about the neural processing of self-produced sound. This includes work on corollary discharge or efference copy, as well as studies showing cortical suppression during vocalizing. The chapter addresses the topic of vocal learning and the general question about the relationship between speech perception and speech production. One of the key requirements for successful reinforcement learning is exploration. Sampling the control space allows the organism to learn the value of a range of different actions.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.013
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0130.002

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 head0.034
GPT teacher head0.321
Teacher spread0.287 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2021
Admission routes1
Has abstractyes

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