Optimizing Acoustic Feature Selection for Estimating Speaker Traits: A Novel Threshold-Based Approach
Bibliographic record
Abstract
Speech signals offer a rich array of information about a speaker, encompassing physical attributes and emotional or health states, with significant applications in forensics, security, surveillance, marketing, and customer service.This work aims to identify key acoustic features for estimating an unidentified speaker's height, age, and gender.A novel Forward Feature Selection with Threshold-Based Backward Elimination (FFS-TBE) algorithm is proposed, designed to optimize feature selection across various spectral, temporal, and prosodic dimensions of speech, including Mel-frequency cepstral coefficients (MFCCs), pitch, and formants.Tested against the TIMIT dataset, the FFS-TBE algorithm surpassed traditional feature selection methods like backward and forward sequential feature selection (BSFS/FSFS) and mutual information (MI) statistical methods.It achieved state-of-the-art results, with mean absolute errors (MAEs) of 4.87 cm for male and 4.5 cm for female speakers in height estimation, and MAEs of 4.82 years and 4.91 years for male and female speakers, respectively, in age estimation.Gender prediction accuracy reached 99%.Crucially, the study found that gender-specific feature selection enhances performance, highlighting the distinct acoustic differences between male and female speakers.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".