Self-supervised learning framework for speaker localisation with a humanoid robot
Bibliographic record
Abstract
Locating a speaker in the space is a skill that plays an essential role in conducting smooth and natural social interactions. Equipping robots with this ability could lead to more fluid human-robot interaction, also by facilitating voice recognition in noisy environments. Most recently proposed sound localisation systems rely on model-based approaches. However, their performances depend on carefully chosen parameters, especially in the binaural and noisy settings typical of humanoids setups. The need for fine-tuning and for adaptation when facing new environments represents a considerable obstacle to the use and portability of such systems in real human-robot interaction scenarios. To overcome these limitations we propose to rely on data-driven approaches (i.e., deep learning) and exploit multi-sensory mechanisms to leverage the direct experience sensed by the robot during an interaction. Taking inspiration from how humans use vision to calibrate their auditory space representation through experiences, we enabled the robot to learn to localize a speaker in a self-supervised way. Our results show that this approach is suitable to learn to localise speakers in the challenging environments typical of human-robot collaboration.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".