Combining Vocal Source and MFCC Features for Enhanced Speaker Recognition Performance Using GMMs
Why this work is in the frame
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Bibliographic record
Abstract
This work presents seven novel spectral features for speaker recognition. These features are the spectral centroid (SC), spectral bandwidth (SBW), spectral band energy (SBE), spectral crest factor (SCF), spectral flatness measure (SFM), Shannon entropy (SE) and Renyi entropy (RE). The proposed spectral features can quantify some of the characteristics of the vocal source or the excitation component of speech. This is useful for speaker recognition since vocal source information is known to be complementary to the vocal tract transfer function, which is usually obtained using the Mel frequency cepstral coefficients (MFCC) or linear predication cepstral coefficients (LPCC). To evaluate the performance of the spectral features, experiments were performed using a text-independent cohort Gaussian mixture model (GMM) speaker identification system. Based on 623 users from the TIMIT database, the spectral features achieved an identification accuracy of 99.33% when combined with the MFCC based features and when using undistorted speech. This represents a 4.03% improvement over the baseline system trained with only MFCC and ΔMFCC features.
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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 it