评估神经说话人验证模型在训练和推理中的能耗与碳排放
Assessing the Energy and Carbon Emissions of Neural Speaker Verification Model in Training and Inference
AI总结：
本研究通过测量不同ResNet架构在VoxCeleb2上的能耗与碳排放，发现模型加深或加宽带来边际精度提升但能耗剧增，而中等规模网络（如ResNet-50）能实现性能与环境影响的良好平衡。
摘要：
深度学习说话人验证（SV）越来越依赖于深度神经网络骨干，但其环境影响仍缺乏记录。本文对在VoxCeleb2上训练的ResNet架构进行了评估，变化深度、通道宽度和阶段分布，并使用节点级传感器测量能耗和碳足迹。结果显示明显的收益递减点：更深或更宽的模型仅带来边际精度提升，而能耗急剧增长。相比之下，中等规模网络如ResNet-50和阶段集中变体在性能与环境影响之间实现了有利的权衡。这些发现为设计节能的SV系统提供了可操作的指导方针。
英文摘要：
Deep-learning speaker verification (SV) increasingly relies on deep neural network backbones, whose environmental impact remains largely undocumented. In this paper, we conduct an evaluation of ResNet architectures trained on VoxCeleb2, varying depth, channel width, and stage distribution, and measure energy consumption and carbon footprint using node-level sensors. Results show a clear point of diminishing returns: deeper or wider models bring only marginal accuracy gains while energy consumption grows steeply. In contrast, mid-sized networks such as ResNet-50 and stage-concentrated variants achieve favorable trade-offs between performance and environmental impact. These findings provide actionable guidelines for designing energy-efficient SV systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
