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Study finds mid-sized neural networks best for energy-efficient speaker verification

By PulseAugur Editorial · [1 sources] ·

A new research paper evaluates the environmental impact of neural speaker verification models, focusing on energy consumption and carbon emissions during training and inference. The study analyzed ResNet architectures on the VoxCeleb2 dataset, finding that deeper or wider models offer minimal accuracy improvements while significantly increasing energy use. The research suggests that mid-sized networks like ResNet-50 provide a better balance between performance and environmental sustainability, offering guidelines for more energy-efficient system design. AI

IMPACT Provides guidelines for developing more sustainable AI systems by optimizing model size for speaker verification tasks.

RANK_REASON The cluster contains an academic paper detailing research findings on model efficiency. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Study finds mid-sized neural networks best for energy-efficient speaker verification

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The cluster contains an academic paper detailing research findings on model efficiency. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

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

    Assessing the Energy and Carbon Emissions of Neural Speaker Verification Model in Training and Inference

    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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