Age-Invariant Speaker Embedding for Diarization of Cognitive Assessments
Bibliographic record
Abstract
This paper investigates an age-invariant speaker embedding approach to speaker diarization, which is an essential step towards the automatic cognitive assessments from speech. Studies have shown that incorporating speaker traits (e.g., age, gender, etc.) can improve speaker diarization performance. However, we found that age information in the speaker embeddings is detrimental to speaker diarization if there is a severe mismatch between the age distributions in the training data and test data. To minimize the detrimental effect of age mismatch, an adversarial training strategy is introduced to remove age variability from the utterance-level speaker embeddings. Evaluations on an interactive dialog dataset for Montreal cognitive assessments (MoCA) show that the adversarial training strategy can produce age-invariant embeddings and reduce diarization error rate (DER) by 4.33%. The approach also outperforms the conventional method even with less training data.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".