Efficient Streaming Speech Quality Prediction with Spiking Neural Networks

As speech processing systems become more ubiquitous, the need for real-time, efficient speech quality prediction (SQP) is growing. Conventional artificial neural networks (ANNs) offer strong prediction performance but can be computationally demanding, which limits their deployment on mobile and edge devices. Spiking neural networks (SNNs) present a promising alternative for ultra-low-power, streaming inference due to their sparse activity and event-driven processing. However, their potential for SQP remains largely unexplored. This article introduces deep convolutional SNNs for SQP and evaluates their performance against state-of-the-art ANN models. Our results show that SNNs achieve comparable accuracy while significantly reducing computational cost. These findings highlight the potential of SNNs to enable real-time, energy-efficient SQP in resource-constrained settings.