Spectro-temporal features for robust far-field speaker identification
Bibliographic record
Abstract
Features derived from an auditory spectro-temporal represen-tation of speech are proposed for robust far-field speaker iden-tification. The auditory representation is obtained by first filtering the speech signal with a gammatone filterbank. A modulation filterbank is then applied to the temporal enve-lope of each gammatone filter output. Compared to com-monly used mel-frequency cepstral coefficients (MFCC), the proposed features are shown to be more robust to mismatched conditions between enrollment and test data and are less sen-sitive to increasing reverberation time (RT). Experiments with simulated and recorded far-field speech show that a Gaus-sian mixture model based identification system, trained on the proposed features, attains an average improvement in identifi-cation accuracy of 15 % relative to a system trained on MFCC. Improvements of up to 85 % are attained for larger RT.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".