Towards Neurocomputational Speech and Sound Processing.
Bibliographic record
Abstract
Abstract. From physiology we learn that the auditory system extracts simultaneous features from the underlying signal, giving birth to simul-taneous representations of audible signals. We also learn that pattern analysis and recognition are not separated processes (in opposition to the engineering approach of pattern recognition where analysis and recogni-tion are usually separated processes). Furthermore, in the visual system, it has been observed that the sequence order of firing is crucial to perform fast visual recognition tasks (Rank Order Coding). The use of the Rank Order Coding has also been recently hypothesized in the mammalian au-ditory system. In a first application we compare a very simplistic speech recognition prototype that uses the Rank Order Coding with a conven-tional Hidden Markov Model speech recognizer. It is also shown that the type of neurons being used should be adapted to the type of phonemes (consonants/transients or vowels/stable) to be recognized. In a second application, we combine a simultaneous auditory images representation with a network of oscillatory spiking neurons to segregate and bind auditory objects for acoustical source separation. It is shown that the spiking neural network performs unsupervised auditory images segmentation (to find ’auditory ’ objects) and binding of the objects be-longing to the same auditory source (yielding automatic sound source separation).
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".