Study Finds Diminishing Returns in Larger Neural Speaker Verification Models, Recommends Mid-Sized Networks for Energy Efficiency
Researchers evaluated the energy consumption and carbon footprint of different ResNet architectures trained for speaker verification tasks, finding that larger models provide only marginal accuracy improvements while consuming significantly more energy. The study measured performance across varying model depths and widths using node-level sensors on the VoxCeleb2 dataset. The findings suggest that mid-sized networks like ResNet-50 offer better trade-offs between accuracy and environmental impact, providing practical guidance for designing sustainable AI systems.
A new study accepted to Speaker Odyssey 2026 examined the environmental cost of deep neural networks used in speaker verification, a technology that identifies individuals by their voice patterns. Researchers tested various ResNet architectures with different depths, channel widths, and stage distributions, measuring both energy consumption and carbon emissions during training and inference. The analysis revealed a clear pattern: as models grew deeper or wider, energy requirements increased steeply while accuracy gains became marginal. Mid-sized architectures, particularly ResNet-50 and stage-concentrated variants, emerged as optimal choices, delivering competitive performance without excessive computational overhead. These findings address a gap in the literature regarding the environmental impact of speaker verification systems and offer practical recommendations for practitioners seeking to balance model performance with sustainability concerns.
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- arXiv cs.CLCenter
Assessing the Energy and Carbon Emissions of Neural Speaker Verification Model in Training and Inference
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