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Evolution of speech rhythm: a cross-species perspective

2019· preprint· en· W2946628395 on OpenAlexaff
Andrea Ravignani, Simone Dalla Bella, Simone Falk, Chris Kello, Florencia Noriega, Sonja A. Kotz

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAnimal Vocal Communication and Behavior
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsRhythmPerspective (graphical)CognitionPerceptionCognitive psychologyRange (aeronautics)CommunicationPsychologyBiologyCognitive scienceComputer scienceNeuroscienceArtificial intelligence

Abstract

fetched live from OpenAlex

Cognition and communication, at the core of human speech rhythm, do not leave a fossil record. However, if the purpose is to understand the origin and evolution of speech rhythm, alternative methods are available. A powerful tool is comparative approach: studying the presence or absence of cognitive/behavioral traits in other species, drawing conclusions on which traits are shared between species, and which are recent human inventions. Here we apply this approach to traits related to human speech rhythm. Many species exhibit temporal structure in their vocalizations but little is known about the range of rhythmic structures perceived and produced, their biological and developmental bases, and communicative functions. We review the literatures on human and non-human studies of rhythm in speech and animal vocalizations to survey similarities and differences. We report important links between vocal perception and motor coordination, and the differentiation of rhythm based on hierarchical temporal structure. We extend this review to quantitative techniques useful for computing rhythmic structure in acoustic sequences and hence facilitating cross-species research. While still far from a full comparative cross-species perspective of speech rhythm, we are closer to fitting missing pieces of the puzzle.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.376
Threshold uncertainty score0.622

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.034
GPT teacher head0.334
Teacher spread0.300 · 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

Citations3
Published2019
Admission routes1
Has abstractyes

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