Deep Learning Models in Speech Recognition: Measuring GPU Energy Consumption, Impact of Noise and Model Quantization for Edge Deployment

arXiv:2405.01004v1 Announce Type: cross
Abstract: Recent transformer-based ASR models have achieved word-error rates (WER) below 4%, surpassing human annotator accuracy, yet they demand extensive server resources, contributing to significant carbon footprints. The traditional server-based architecture of ASR also presents privacy concerns, alongside reliability and latency issues due to network dependencies. In contrast, on-device (edge) ASR enhances privacy, boosts performance, and promotes sustainability by effectively balancing energy use and accuracy for specific applications. This study examines the effects of quantization, memory …

arxiv consumption cs.ai cs.cv cs.hc cs.lg cs.sd deep learning deployment edge eess.as energy gpu impact measuring noise quantization recognition speech speech recognition type