Automatic Speaker Identification from Interpersonal Synchrony of Body Motion Behavioral Patterns in Multi-Person Videos
Bibliographic record
Abstract
Interpersonal synchrony, i.e. the temporal coordination of persons during social interactions, was traditionally studied by developmental psychologists. It now holds an important role in fields such as social signal processing, usually treated as a dyadic issue. In this paper, we focus on the behavioral patterns from body motion to identify subtle social interactions in the context of multi-person discussion panels, typically involving more than two interacting individuals. We propose a computer-vision based approach for automatic speaker identification that takes advantage of body motion interpersonal synchrony between participants. The approach characterizes human body motion with a novel feature descriptor based on the pixel change history of multiple body regions, which is then used to classify the motor behavioral patterns of the participants into speaking/non-speaking. Our approach was evaluated on a challenging dataset of video segments from discussion panel scenes collected from YouTube. Results are very promising and suggest that interpersonal synchrony of motion behavior is indeed indicative of speaker/listener roles.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".