Margin-Guided Adaptive Model Routing for Resource-Efficient Speaker Verification on Edge Devices

Resource-constrained edge deployment of speaker verification systems requires reducing inference cost without unnecessarily invoking a high-capacity model. This study presents a margin-guided adaptive model-routing framework in which ReDimNet-b0 serves as a compact verifier and ReDimNet-b6 as an expert, with routing parameters derived from development and calibration data and frozen before evaluation. On development trials, the compact-model decision margin predicted expert-benefit cases with a ROC-AUC of 0.9797, and the lowest-margin 10% region captured 97.25% of such cases. On the 37,720-trial VoxCeleb1-O protocol, Frozen-B10 invoked ReDimNet-b6 on 9.653% of trials and achieved an EER of 0.7238%, compared with 1.5403% for static ReDimNet-b0 and 0.7158% for static ReDimNet-b6. However, its normalized minDCF was 0.1352, compared with 0.1640 and 0.0543 for the compact and expert models, respectively, showing that the near-expert EER does not extend to the low-false-alarm operating region. A 2000-replicate paired speaker-dependent bootstrap yielded a 95% confidence interval of [−0.0097,0.1420] percentage points for the EER difference between Frozen-B10 and ReDimNet-b6. This interval does not establish equivalence or non-inferiority, whereas the corresponding minDCF-difference interval remained strictly positive. On USC-SV TEST, source-frozen routing increased expert utilization from 9.653% on VoxCeleb1-O to 31.336%; system-specific decision thresholds were independently estimated on speaker-disjoint USC-SV CAL. In a cached-enrollment Raspberry Pi 5 benchmark, Frozen-B10 used the expert on 8% of trials and achieved a mean latency of 1.716 s per trial, reducing latency by 83.52% relative to unconditional sequential execution and by 81.84% relative to static ReDimNet-b6. Direct system-level energy measurements further showed consumption of 10.608±0.042 J per trial, corresponding to reductions of 84.47% relative to sequential execution and 83.08% relative to static ReDimNet-b6. Substantial latency savings persisted up to 38% expert utilization, whereas process-isolated measurements showed no corresponding RAM reduction because both models remain resident. Overall, margin-guided routing concentrates expensive expert computation on a small subset of trials and substantially reduces measured edge-device latency and system-level energy, while its accuracy–efficiency trade-off remains operating-point, domain, and deployment-dependent.