Non-Contrastive vs. Contrastive Self-Supervised Speech Models in Speaker Verification Throughput

This report synthesises findings from 10 peer-reviewed papers addressing the following research question: How does the inference throughput of non-contrastive self-supervised speech models compare to contrastive methods like LightGCL when evaluated on utterance-level speaker verification benchmarks. 6 claims were extracted from source literature; 6 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.5/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research goal: How does the inference throughput of non-contrastive self-supervised speech models compare to contrastive methods like LightGCL when evaluated on utterance-level speaker verification benchmarks?

Autonomous literature synthesis. Automated review score: 8.5/10. Full text and citation available at Assignee Research.