Classification Vectors as a Tool for Modelling Human Speech Perception
Bibliographic record
Abstract
Humans perceive speech through the lens of their native phonemic categories, which fundamentally shape both the identification and discrimination of speech sounds. This connection between how we identify and discriminate phonemes is widely recognised as the phenomenon of Categorical Perception. We present two studies that quantitatively explore the role of categorical perception in human speech perception, focusing on how native phonemic categories influence the identification and discrimination of speech sounds when comparing an approach which operationalises categorical perception compared to one that does not. In Study 1, we apply classification vectors to model English-listener identification experiments, using Mel-Frequency Cepstral Coefficients (MFCCs) and other input representations such as wav2vec 2.0 and DeepSpeech2. Study 2 extends this approach to English-listener discrimination experiments. Our findings indicate that computational models, particularly those using wav2vec 2.0, offer more precise simulations of human speech perception surpassing acoustic-only models. By demonstrating that these classification vectors can effectively model both identification and discrimination tasks, this research provides a quantitative framework that solidifies the importance of categorical perception in human speech perception.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".