Text-Independent Speaker Verification Employing A Novel Hybrid Neural Embedding Extractor
Bibliographic record
Abstract
Reliable and discriminative speaker embedding extraction lies at the heart of modern neural automatic speaker verification (ASV) systems. In this study, we introduce a novel hybrid neural architecture that enhances embedding quality by integrating frequency- and channel-aware Selective Kernel Attention (SKA) into a 2D convolutional neural network (2D-CNN) feature extractor. This design strengthens the joint modeling of frequency and channel characteristics, resulting in more discriminative speaker representations. The feature extractor feeds into a composite frame-level network, structured as a cascade of Time-Delay Neural Network (TDNN)–Long Short-Term Memory (LSTM) hybrid and fully TDNN layers. To summarize speaker traits at the utterance level, we employ Multi-Level Attentive Statistics Pooling (MLASP), which captures diverse statistical cues and exploits the complementary strengths of the hybrid architecture. MLASP further improves robustness by recovering subtle, previously underutilized features. The full system is trained using the additive angular margin softmax (AAMSoftmax) loss, which promotes tighter intra-speaker clustering and broader inter-speaker separation in the embedding space. We also explore the influence of different CNN-driven feature learning modules on ASV performance and resilience. Evaluations on the VoxCeleb and CNCeleb benchmarks confirm that our proposed method consistently surpasses both standard baselines and state-of-the-art ASV models trained under the same conditions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".