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English(EN) Assessing the Energy and Carbon Emissions of Neural Speaker Verification Model in Training and Inference

研究发现中等规模神经网络最适合能效高的说话人验证

作者 PulseAugur 编辑部 · [1 个来源] ·

一篇新研究论文评估了神经说话人验证模型对环境的影响，重点关注训练和推理过程中的能耗和碳排放。该研究在VoxCeleb2数据集上分析了ResNet架构，发现更深或更宽的模型在准确性方面提升甚微，但能耗却显著增加。研究表明，像ResNet-50这样的中等规模网络在性能和环境可持续性之间提供了更好的平衡，为设计更节能的系统提供了指导。 AI

影响 通过优化说话人验证任务的模型规模，为开发更可持续的AI系统提供了指导。

排序理由 该集群包含一篇详细介绍模型效率研究结果的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

研究发现中等规模神经网络最适合能效高的说话人验证

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该集群包含一篇详细介绍模型效率研究结果的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CL TIER_1 English(EN) · Mickael Rouvier ·

    评估神经说话人验证模型在训练和推理中的能源和碳排放

    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 dis…

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