The processing of intimately familiar and unfamiliar voices: Specific neural responses of speaker recognition and identification
Bibliographic record
Abstract
Research has repeatedly shown that familiar and unfamiliar voices elicit different neural responses. But it has also been suggested that different neural correlates associate with the feeling of having heard a voice and knowing who the voice represents. The terminology used to designate these varying responses remains vague, creating a degree of confusion in the literature. Additionally, terms serving to designate tasks of voice discrimination, voice recognition, and speaker identification are often inconsistent creating further ambiguities. The present study used event-related potentials (ERPs) to clarify the difference between responses to 1) unknown voices, 2) trained-to-familiar voices as speech stimuli are repeatedly presented, and 3) intimately familiar voices. In an experiment, 13 participants listened to repeated utterances recorded from 12 speakers. Only one of the 12 voices was intimately familiar to a participant, whereas the remaining 11 voices were unfamiliar. The frequency of presentation of these 11 unfamiliar voices varied with only one being frequently presented (the trained-to-familiar voice). ERP analyses revealed different responses for intimately familiar and unfamiliar voices in two distinct time windows (P2 between 200-250 ms and a late positive component, LPC, between 450-850 ms post-onset) with late responses occurring only for intimately familiar voices. The LPC present sustained shifts, and short-time ERP components appear to reflect an early recognition stage. The trained voice equally elicited distinct responses, compared to rarely heard voices, but these occurred in a third time window (N250 between 300-350 ms post-onset). Overall, the timing of responses suggests that the processing of intimately familiar voices operates in two distinct steps of voice recognition, marked by a P2 on right centro-frontal sites, and speaker identification marked by an LPC component. The recognition of frequently heard voices entails an independent recognition process marked by a differential N250. Based on the present results and previous observations, it is proposed that there is a need to distinguish between processes of voice "recognition" and "identification". The present study also specifies test conditions serving to reveal this distinction in neural responses, one of which bears on the length of speech stimuli given the late responses associated with voice identification.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".