Speech imitation: the cognitive underpinnings of adaptive vocal behaviour
Bibliographic record
Abstract
Speech imitation appears to be one of the most fundamental aspects of human vocal behavior. It has been suggested that it plays an important role in speech development and may also form one of the key mechanisms that underlie the emergence and evolution of human languages. Starting early on, infants appear to be matching the prosodic and micro-prosodic properties of their mothers' speech in child-mother interactions. Also in the course of a conversational interaction between mature talkers, the behavior of each talker tends to become more similar-sounding to that of the conversational partner. The behavioral tendency shown by humans to imitate others may be connected at the brain level with the presence of a mirror neuron system, whose discovery has raised important issues about the role that this action-observation matching system may fulfill in many different domains, from sensorimotor integration to the understanding of others' behavior. The focus of this Research Topic is the fast-growing body of research on imitation phenomena in speech. We aim to bring together researchers with a large variety of scientific backgrounds (linguistics, speech sciences, psycholinguistics, experimental sociolinguistics, neurosciences, cognitive sciences) with a view to improving our understanding of the role of imitation in the production, comprehension and acquisition of spoken language. The Research Topic will also aim to assess current research on the brain and cognitive underpinnings of imitative behavior. The questions that can be explored in the submitted articles and communications include the following: When is phonetic imitation an automatic process and when does it represent a conscious effort of the talkers, designed to fit the social context of the interaction? Which components of the speech signal are imitated in different contexts/conditions? What are the causes of individual differences in the ability to imitate phonetic properties of both L1 and L2 (e.g., phonetic talent, dominant pitch perception mode, auditory memory etc.)? What computational techniques can be successfully employed to characterize imitation in speech, both in terms of static characterization (comparing short fragments of speech), as well as from a nonsegmental perspective (comparing evolution of different features over time)? How do visual cues interact with auditory cues, e.g., with respect to the degree of imitation and the speed of processing? What are the neural correlates of speech imitation?
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".