keynotes
Bibliographic record
Abstract
Listening in noisy and crowded environments is a challenging task. Assistive hearing devices can suppress certain types of background noise, but they cannot help a user focus on a single conversation amongst many without knowing which speaker is the target. Our recent scientific discoveries of speech processing in the human auditory cortex have motivated several new paths to enhance the efficacy of hearable technologies. These possibilities include, I) speech neuroprosthesis which aims to establish a direct communication channel with the brain, II) auditory attention decoding where the similarity of a listener's brainwave to the sources in the acoustic scene is used to identify the target source, and III) increased speech perception using electrical brain stimulation. In parallel, the field of auditory scene analysis has recently seen great progress due to the emergence of deep learning models, where even solving the multi-talker speech recognition is no longer out of reach. I will discuss our recent efforts in bringing together the latest progress in speech neurophysiology, brain-computer interfaces, and speech processing technologies to design and actualize the next generation of assistive hearing devices, with the potential to augment speech communication in realistic and challenging acoustic conditions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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".