A Hybrid Neural Approach to Speaker Verification with an Improved Additive Angular Margin Loss
Bibliographic record
Abstract
Extraction of speaker embeddings plays a crucial role in the neural automatic speaker verification system. Here, we propose a novel hybrid neural embedding framework, which employs frequency- and channel-wise Selective Kernel Attention (SKA) into the 2D-CNN - based feature extraction module to aggregate global frequency-channel information to the attention weights to extract speaker discriminant embeddings. The aforementioned feature extraction module is connected with a frame-level network, which is composed of a Time Delay Neural Network (TDNN)-Long Short Term Memory hybrid network and a fully TDNN network in a cascade fashion. Multi-Level Attentive Statistics Pooling, which incorporates local statistics as context, is adopted for aggregating the speaker information within an utterance-level context by capturing the complementarity of different networks. Additionally, the proposed approach utilizes an improved Additive Angular Margin (AAM) Softmax loss function that integrates a dynamic and adaptive label noise cleansing method, termed AdaptiveDrop. This method seamlessly combines label noise filtering and correction in a cascaded manner, leveraging the strengths of both techniques to enhance robustness. Experimental results on the VoxCeleb dataset reveal that the proposed approach outperforms baseline systems in both supervised and self-supervised speaker verification tasks.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".