A Fractional Ebola Optimization Search Algorithm Approach for Enhanced Speaker Diarization
Bibliographic record
Abstract
Speaker diarization, the task of ascertaining speaker homogeneity within a collection of audio recordings featuring multiple speakers, is crucial for answering queries such as "who spoke when".Diverse speaker recordings, encompassing meetings, reality shows, and news broadcasts, typically populate the speaker diarization database.Traditional methods primarily rely on clustering speaker embeddings, yet these approaches often fail to minimize diarization errors effectively and struggle to accurately account for speaker overlaps.Addressing these limitations, we propose a robust model leveraging the Fractional Ebola Optimization Search Algorithm (FEOSA) for speaker segmentation and diarization.This model represents an amalgamation of the Fractional Calculus (FC) concept and the Ebola Optimization Search Algorithm (EOSA), thereby enhancing the efficacy of the diarization process.The diarization task is executed employing an entropy weighted power k-means algorithm, with weights updated via the proposed FEOSA.The proposed FEOSA demonstrated superior testing accuracy, reaching a maximum of 0.913, and significantly reduced diarization errors to a minimum of 0.566.Further, False Discovery Rate (FDR), False Negative Rate (FNR) and False Positive Rate (FPR) were recorded at 0.257, 0.128, and 0.104 respectively, underscoring the effectiveness of the proposed model in enhancing speaker diarization.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| 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".