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