A Novel Hybrid Neural Embedding Extractor for Text Independent Speaker Verification
Bibliographic record
Abstract
Speaker embedding extraction is crucial for neural automatic speaker verification systems. In this work, we propose a novel hybrid neural embedding framework that integrates frequency- and channel-wise Selective Kernel Attention (SKA) into a 2D-CNN-based feature extraction module. This integration improves the aggregation of frequency-channel information, enhancing the extraction of discriminative speaker embeddings. The feature extraction module is connected to a frame-level network, combining a Time-Delay Neural Network (TDNN)-Long Short-Term Memory (LSTM) hybrid with a fully TDNN network in a cascaded structure. To capture speaker information at the utterance level, we use Multi-Level Attentive Statistics Pooling (MLASP), which incorporates local statistics and leverages the complementarity of different networks. MLASP also helps integrate previously overlooked features, enhancing the robustness of the learned embeddings. The entire framework is trained with the additive angular margin softmax (AAMSoftmax) objective, creating an embedding space where embeddings of the same speaker are close together, and those of different speakers are well separated. Experimental results on the VoxCeleb and CNCeleb corpora demonstrate that our approach outperforms both baseline and state-of-the-art speaker verification systems trained on the same datasets.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".