Subspace-based Speaker-independent Vowel Recognition
Bibliographic record
Abstract
The subspaces representing two different vowel classes may have a large common subspace due to speaker variability, noise and coarticulation. We use common principal component (CPC) and its extension i.e., partial-CPC (pCPC) to obtain a specific subspace for each vowel which is insensitive to variations. We perform CPC analysis on the covariance matrices of the vowels. An input vector from an unknown vowel is classified based on the maximum length of its projection on the specific subspaces. We have chosen 18D mel-frequency cepstral coefficients as a feature in our recognition task. The specific subspace is treated as a transformation matrix which enhances the vowel-specific information in the feature vector and, in turn, increases SNR. Experiments were performed on vowels extracted from a multiple speaker set taken from different dialect regions. The results are encouraging in the context of a speaker-independent framework. Visual analysis of the vowel basis spectra provides useful and interesting information by highlighting the importance of different frequency regions.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".