Feature fusion techniques based training MLP for speaker identification system
Bibliographic record
Abstract
This paper aims to compare the Linear Predictive Cepstral Coefficients (LPCC) method, the Mel-frequency Cepstral Coefficient (MFCC) method, their concatenation (LPCC-MFCC), and a new proposed feature fusion approach based on method involving this concatenation with the respective averages normalization; Linear predictive and Mel-frequency Cepstral Coefficients (LMACC) through applying a multi-layer perceptron (MLP) neural network as classifier for speaker identification system (SIS). The evaluation was made based on classification accuracy. After evaluation, the results of the proposed system LMACC-MLP were verified using Cochlear implant-like spectrally reduced speech (SRS) algorithm proposed in the literature so that the original recorded signal was resynthesized based on an acoustic simulation of the cochlear implant. i.e. human speech perception. Our proposed system demonstrated maximal relative performance in environments on measures of recognition rate, compared with other methods and covering a range of different root- mean - square (RMS) noise amplitudes.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 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".